Dataset Viewer
text
stringlengths 189
2.12k
| source
stringclasses 1
value |
|---|---|
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following scheduler policies are preemptive?
A. FIFO (First In, First Out)
B. SJF (Shortest Job First)
C. STCF (Shortest Time to Completion First)
D. RR (Round Robin)
Answer: C,D
Details: STCF (Shortest Time to Completion First).RR (Round Robin) | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following are correct implementation for acquire function ? Assume 0 means UNLOCKED and 1 means LOCKED. Initially l->locked = 0.
A. c
void acquire(struct lock *l)
{
for(;;)
if(xchg(&l->locked, 1) == 0)
return;
}
B. c
void acquire(struct lock *l)
{
if(cas(&l->locked, 0, 1) == 0)
return;
}
C. c
void acquire(struct lock *l)
{
for(;;)
if(cas(&l->locked, 1, 0) == 1)
return;
}
D. c
void acquire(struct lock *l)
{
if(l->locked == 0)
return;
}
Answer: A
Details: c
void acquire(struct lock *l)
{
for(;;)
if(xchg(&l->locked, 1) == 0)
return;
} | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In which of the following cases does JOS acquire the big kernel lock?
A. Processor traps in user mode
B. Processor traps in kernel mode
C. Switching from kernel mode to user mode
D. Initialization of application processor
Answer: A,D
Details: Processor traps in user mode.Initialization of application processor | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Assume a user program executes following tasks. Select all options that will use a system call.
A. Read the user's input "Hello world" from the keyboard.
B. Write "Hello world" to a file.
C. Encrypt "Hello world" by AES.
D. Send "Hello world" to another machine via Network Interface Card.
Answer: A,B,D
Details: Read the user's input "Hello world" from the keyboard..Write "Hello world" to a file..Send "Hello world" to another machine via Network Interface Card. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: What are the drawbacks of non-preemptive scheduling compared to preemptive scheduling?
A. It can lead to starvation especially for those real-time tasks
B. Less computational resources need for scheduling and takes shorted time to suspend the running task and switch the context.
C. Bugs in one process can cause a machine to freeze up
D. It can lead to poor response time for processes
Answer: A,C,D
Details: It can lead to starvation especially for those real-time tasks.Bugs in one process can cause a machine to freeze up.It can lead to poor response time for processes | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Select valid answers about file descriptors (FD):
A. The value of FD is unique for every file in the operating system.
B. FD is usually used as an argument for read and write.
C. FD is constructed by hashing the filename.
D. FDs are preserved after fork() and can be used in the new process pointing to the original files.
Answer: B,D
Details: FD is usually used as an argument for read and write..FDs are preserved after fork() and can be used in the new process pointing to the original files. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Suppose a file system used only for reading immutable files in random fashion. What is the best block allocation strategy?
A. Linked-list allocation
B. Continuous allocation
C. Index allocation with B-tree
D. Index allocation with Hash-table
Answer: B
Details: Continuous allocation | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following operations would switch the user program from user space to kernel space?
A. Dividing integer by 0.
B. Calling sin() in math library.
C. Invoking read() syscall.
D. Jumping to an invalid address.
Answer: A,C,D
Details: Dividing integer by 0..Invoking read() syscall..Jumping to an invalid address. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which flag prevents user programs from reading and writing kernel data?
A. PTE_P
B. PTE_U
C. PTE_D
D. PTE_W
Answer: B
Details: PTE_U | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In which of the following cases does the TLB need to be flushed?
A. Inserting a new page into the page table for a user-space application.
B. Deleting a page from the page table.
C. Changing the read/write permission bit in the page table.
D. Inserting a new page into the page table for kernel.
Answer: B,C
Details: Deleting a page from the page table..Changing the read/write permission bit in the page table. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In x86, select all synchronous exceptions?
A. Divide error
B. Timer
C. Page Fault
D. Keyboard
Answer: A,C
Details: Divide error.Page Fault | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the execution of an application are possible on a single-core machine?
A. Concurrent execution
B. Parallel execution
C. Both concurrent and parallel execution
D. Neither concurrent or parallel execution
Answer: A
Details: Concurrent execution | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In an x86 multiprocessor system with JOS, select all the correct options. Assume every Env has a single thread.
A. Two Envs could run on the same processor simultaneously.
B. Two Envs could run on two different processors simultaneously.
C. One Env could run on two different processors simultaneously.
D. One Env could run on two different processors at different times.
Answer: B,D
Details: Two Envs could run on two different processors simultaneously..One Env could run on two different processors at different times. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In JOS, suppose a value is passed between two Envs. What is the minimum number of executed system calls?
A. 1
B. 2
C. 3
D. 4
Answer: B
Details: 2 | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: What strace tool does?
A. It prints out system calls for given program. These system calls are always called when executing the program.
B. It prints out system calls for given program. These systems calls are called only for that particular instance of the program.
C. To trace a symlink. I.e. to find where the symlink points to.
D. To remove wildcards from the string.
Answer: B
Details: It prints out system calls for given program. These systems calls are called only for that particular instance of the program. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: What is a good distance metric to be used when you want to compute the similarity between documents independent of their length?A penalty will be applied for any incorrect answers.
A. Cosine similarity
B. Euclidean distance
C. Manhattan distance
D. Chi-squared distance
Answer: A
Details: Cosine similarity | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: For this question, one or more assertions can be correct. Tick only the correct assertion(s). There will be a penalty for wrong assertions ticked.Which of the following associations can be considered as illustrative
Use this example only if it is relevant to answer precisely the question at the bottom.
examples for inflectional
morphology (with here the simplifying assumption that canonical forms are restricted to the roots
only)?
A. (activate, action)
B. (hypothesis, hypotheses)
C. (to go, went)
D. (speaking, talking)
Answer: B,C
Details: (hypothesis, hypotheses).(to go, went) | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following statements are true?
A. The more training
Use this example only if it is relevant to answer precisely the question at the bottom.
examples, the more accurate the prediction of a $k$-nearest-neighbor classifier.
B. k-nearest-neighbors cannot be used for regression.
C. A $k$-nearest-neighbor classifier is sensitive to outliers.
D. Training a $k$-nearest-neighbor classifier takes more computational time than applying it / using it for prediction.
Answer: A,C
Details: The more training
Use this example only if it is relevant to answer precisely the question at the bottom.
examples, the more accurate the prediction of a $k$-nearest-neighbor classifier..A $k$-nearest-neighbor classifier is sensitive to outliers. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Let $n$ be an integer such that $n\geq 2$ and let $A \in \R^{n imes n}$, and $xv \in \R^n$, consider the function $f(xv) = xv^ op A xv$ defined over $\R^n$. Which of the following is the gradient of the function $f$?
A. $2 xv^ op A$
B. $2Axv$
C. $A^ op xv + Axv$
D. $2A^ op xv$
Answer: ['$A^\top xv + Axv$$\nabla f (xv)= A^\top xv+Axv$. Here the matrix $A$ is not symmetric.'] | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In Text Representation learning, which of the following statements is correct?
A. Learning GloVe vectors can be done using SGD in a streaming fashion, by streaming through the input text only once.
B. Every recommender systems algorithm for learning a matrix factorization $\boldsymbol{W} \boldsymbol{Z}^{\top}$ approximating the observed entries in least square sense does also apply to learn GloVe word vectors.
C. FastText performs unsupervised learning of word vectors.
D. If you fix all word vectors, and only train the remaining parameters, then FastText in the two-class case reduces to being just a linear classifier.
Answer: B,D
Details: Every recommender systems algorithm for learning a matrix factorization $\boldsymbol{W} \boldsymbol{Z}^{\top}$ approximating the observed entries in least square sense does also apply to learn GloVe word vectors..If you fix all word vectors, and only train the remaining parameters, then FastText in the two-class case reduces to being just a linear classifier. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Consider a matrix factorization problem of the form $\mathbf{X}=\mathbf{W Z}^{\top}$ to obtain an item-user recommender system where $x_{i j}$ denotes the rating given by $j^{\text {th }}$ user to the $i^{\text {th }}$ item . We use Root mean square error (RMSE) to gauge the quality of the factorization obtained. Select the correct option.
A. Given a new item and a few ratings from existing users, we need to retrain the already trained recommender system from scratch to generate robust ratings for the user-item pairs containing this item.
B. Regularization terms for $\mathbf{W}$ and $\mathbf{Z}$ in the form of their respective Frobenius norms are added to the RMSE so that the resulting objective function becomes convex.
C. For obtaining a robust factorization of a matrix $\mathbf{X}$ with $D$ rows and $N$ elements where $N \ll D$, the latent dimension $\mathrm{K}$ should lie somewhere between $D$ and $N$.
D. None of the other options are correct.
Answer: D
Details: None of the other options are correct. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Consider the loss function $L: \R^d o \R$, $L(\wv) = rac{eta}{2}\|\wv\|^2$, where $eta > 0$ is a constant. We run gradient descent on $L$ with a stepsize $\gamma > 0$ starting from some $\wv_0
eq 0$. Which of the statements below is true?
A. Gradient descent converges to the global minimum for any stepsize $\gamma > 0$.
B. Gradient descent with stepsize $\gamma = rac{2}{eta}$ produces iterates that diverge to infinity ($\|\wv_t\| o \infty$ as $t o \infty$).
C. Gradient descent converges in two steps for $\gamma = rac{1}{eta}$ (i.e., $\wv_2$ is the extbf{first} iterate attaining the global minimum of $L$).
D. Gradient descent converges to the global minimum for any stepsize in the interval $\gamma \in ig( 0, rac{2}{eta}ig)$.
Answer: ['Gradient descent converges to the global minimum for any stepsize in the interval $\\gamma \\in \x08ig( 0, \x0crac{2}{\x08eta}\x08ig)$.The update rule is $\\wv_{t+1} = \\wv_t - \\gamma\x08eta \\wv_t = (1 - \\gamma\x08eta) \\wv_t$. Therefore we have that the sequence $\\{\\|\\wv_{t}\\|\\}_t$ is given by $\\|\\wv_{t+1}\\| = \\lvert 1 - \\gamma\x08eta\rvert \\|\\wv_t\\| = \\lvert 1 - \\gamma\x08eta\rvert^t \\|\\wv_0\\|$. We can see that for $\\gamma = \x0crac{2}{\x08eta}$ the elements of the aforementioned sequence never move from $\\|\\wv_0\\|$ (so the algorithm does not diverge to infinity for this stepsize). For $\\gamma = \x0crac{1}{\x08eta}$ the algorithm converges in one step, not two. And finally, for any $\\gamma \\in \\left( 0, \x0crac{2}{\x08eta}\right)$ the algorithm will converge to the global minimum since $\\lvert 1 - \\gamma\x08eta\rvert \\in \\left( 0, 1\right)$.'] | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: You are doing your ML project. It is a regression task under a square loss. Your neighbor uses linear regression and least squares. You are smarter. You are using a neural net with 10 layers and activations functions $f(x)=3 x$. You have a powerful laptop but not a supercomputer. You are betting your neighbor a beer at Satellite who will have a substantially better scores. However, at the end it will essentially be a tie, so we decide to have two beers and both pay. What is the reason for the outcome of this bet?
A. Because we use exactly the same scheme.
B. Because it is almost impossible to train a network with 10 layers without a supercomputer.
C. Because I should have used more layers.
D. Because I should have used only one layer.
Answer: A
Details: Because we use exactly the same scheme. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Let $f:\R^D
ightarrow\R$ be an $L$-hidden layer multi-layer perceptron (MLP) such that
\[
f(xv)=\sigma_{L+1}ig(\wv^ op\sigma_L(\Wm_L\sigma_{L-1}(\Wm_{L-1}\dots\sigma_1(\Wm_1xv)))ig),
\]
with $\wv\in\R^{M}$, $\Wm_1\in\R^{M imes D}$ and $\Wm_\ell\in\R^{M imes M}$ for $\ell=2,\dots, L$, and $\sigma_i$ for $i=1,\dots,L+1$ is an entry-wise activation function. For any MLP $f$ and a classification threshold $ au$ let $C_{f, au}$ be a binary classifier that outputs YES for a given input $xv$ if $f(xv) \leq au$ and NO otherwise. space{3mm}
Assume $\sigma_{L+1}$ is the element-wise extbf{sigmoid} function and $C_{f, rac{1}{2}}$ is able to obtain a high accuracy on a given binary classification task $T$. Let $g$ be the MLP obtained by multiplying the parameters extbf{in the last layer} of $f$, i.e. $\wv$, by 2. Moreover, let $h$ be the MLP obtained by replacing $\sigma_{L+1}$ with element-wise extbf{ReLU}. Finally, let $q$ be the MLP obtained by doing both of these actions. Which of the following is true?
ReLU(x) = max\{x, 0\} \
Sigmoid(x) = rac{1}{1 + e^{-x}}
A. $C_{g, rac{1}{2}}$ may have an accuracy significantly lower than $C_{f, rac{1}{2}}$ on $T$
B. $C_{h, 0}$ may have an accuracy significantly lower than $C_{f, rac{1}{2}}$ on $T$
C. $C_{q, 0}$ may have an accuracy significantly lower than $C_{f, rac{1}{2}}$ on $T$
D. $C_{g, rac{1}{2}}$, $C_{h, 0}$, and $C_{q, 0}$ have the same accuracy as $C_{f, rac{1}{2}}$ on $T$
Answer: ['$C_{g, \x0crac{1}{2}}$, $C_{h, 0}$, and $C_{q, 0}$ have the same accuracy as $C_{f, \x0crac{1}{2}}$ on $T$\n Since the threshold $\x0crac{1}{2}$ for sigmoid corresponds to the input to the last activation function being positive, $C_{h, 0}$ is true. Moreover, multiplying the weights by 2 does not change the sign of the output. Therefore both $C_{g, \x0crac{1}{2}}$ and $C_{q, 0}$ are also true.'] | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is correct regarding Louvain algorithm?
A. It creates a hierarchy of communities with a common root
B. Clique is the only topology of nodes where the algorithm detects the same communities, independently of the starting point
C. If n cliques of the same order are connected cyclically with n-1 edges, then the algorithm will always detect the same communities, independently of the starting point
D. Modularity is always maximal for the communities found at the top level of the community hierarchy
Answer: C
Details: If n cliques of the same order are connected cyclically with n-1 edges, then the algorithm will always detect the same communities, independently of the starting point | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Let the first four retrieved documents be N N R R, where N denotes a non-relevant and R a relevant document. Then the MAP (Mean Average Precision) is:
A. 1/2
B. 5/12
C. 3/4
D. 7/24
Answer: B
Details: 5/12 | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is true?
A. High precision implies low recall
B. High precision hurts recall
C. High recall hurts precision
D. High recall implies low precision
Answer: B,C
Details: High precision hurts recall.High recall hurts precision | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: The inverse document frequency of a term can increase
A. by adding the term to a document that contains the term
B. by removing a document from the document collection that does not contain the term
C. by adding a document to the document collection that contains the term
D. by adding a document to the document collection that does not contain the term
Answer: D
Details: by adding a document to the document collection that does not contain the term | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is wrong regarding Ontologies?
A. We can create more than one ontology that conceptualize the same real-world entities
B. Ontologies help in the integration of data expressed in different models
C. Ontologies support domain-specific vocabularies
D. Ontologies dictate how semi-structured data are serialized
Answer: D
Details: Ontologies dictate how semi-structured data are serialized | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In a Ranked Retrieval result, the result at position k is non-relevant and at k+1 is relevant. Which of the following is always true (P@k and R@k are the precision and recall of the result set consisting of the k top ranked documents)?
A. P@k-1 > P@k+1
B. P@k-1 = P@k+1
C. R@k-1 < R@k+1
D. R@k-1 = R@k+1
Answer: C
Details: R@k-1 < R@k+1 | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: What is true regarding Fagin's algorithm?
A. It performs a complete scan over the posting files
B. It provably returns the k documents with the largest aggregate scores
C. Posting files need to be indexed by TF-IDF weights
D. It never reads more than (kn)½ entries from a posting list
Answer: B
Details: It provably returns the k documents with the largest aggregate scores | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is WRONG for Ontologies?
A. Different information systems need to agree on the same ontology in order to interoperate.
B. They help in the integration of data expressed in different models.
C. They give the possibility to specify schemas for different domains.
D. They dictate how semi-structured data are serialized.
Answer: D
Details: They dictate how semi-structured data are serialized. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: What is the benefit of LDA over LSI?
A. LSI is sensitive to the ordering of the words in a document, whereas LDA is not
B. LDA has better theoretical explanation, and its empirical results are in general better than LSI’s
C. LSI is based on a model of how documents are generated, whereas LDA is not
D. LDA represents semantic dimensions (topics, concepts) as weighted combinations of terms, whereas LSI does not
Answer: B
Details: LDA has better theoretical explanation, and its empirical results are in general better than LSI’s | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Maintaining the order of document identifiers for vocabulary construction when partitioning the document collection is important
A. in the index merging approach for single node machines
B. in the map-reduce approach for parallel clusters
C. in both
D. in neither of the two
Answer: A
Details: in the index merging approach for single node machines | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is correct regarding Crowdsourcing?
A. Random Spammers give always the same answer for every question
B. It is applicable only for binary classification problems
C. Honey Pot discovers all the types of spammers but not the sloppy workers
D. The output of Majority Decision can be equal to the one of Expectation-Maximization
Answer: D
Details: The output of Majority Decision can be equal to the one of Expectation-Maximization | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: When computing PageRank iteratively, the computation ends when...
A. The difference among the eigenvalues of two subsequent iterations falls below a predefined threshold
B. The norm of the difference of rank vectors of two subsequent iterations falls below a predefined threshold
C. All nodes of the graph have been visited at least once
D. The probability of visiting an unseen node falls below a predefined threshold
Answer: B
Details: The norm of the difference of rank vectors of two subsequent iterations falls below a predefined threshold | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: How does LSI querying work?
A. The query vector is treated as an additional term; then cosine similarity is computed
B. The query vector is transformed by Matrix S; then cosine similarity is computed
C. The query vector is treated as an additional document; then cosine similarity is computed
D. The query vector is multiplied with an orthonormal matrix; then cosine similarity is computed
Answer: C
Details: The query vector is treated as an additional document; then cosine similarity is computed | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Suppose that an item in a leaf node N exists in every path. Which one is correct?
A. N co-occurs with its prefix in every transaction.
B. For every node P that is a parent of N in the fp tree, confidence(P->N) = 1
C. N’s minimum possible support is equal to the number of paths.
D. The item N exists in every candidate set.
Answer: C
Details: N’s minimum possible support is equal to the number of paths. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In a Ranked Retrieval result, the result at position k is non-relevant and at k+1 is relevant. Which of the following is always true (P@k and R@k are the precision and recall of the result set consisting of the k top ranked documents)?
A. P@k-1 > P@k+1
B. P@k-1 = P@k+1
C. R@k-1 < R@k+
D. R@k-1 = R@k+1
Answer: C
Details: R@k-1 < R@k+ | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: For the number of times the apriori algorithm and the FPgrowth algorithm for association rule mining are scanning the transaction database the following is true
A. fpgrowth has always strictly fewer scans than apriori
B. fpgrowth and apriori can have the same number of scans
C. apriori cannot have fewer scans than fpgrowth
D. all three above statements are false
Answer: B
Details: fpgrowth and apriori can have the same number of scans | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Given the following teleporting matrix (Ε) for nodes A, B and C:[0 ½ 0][0 0 0][0 ½ 1]and making no assumptions about the link matrix (R), which of the following is correct:(Reminder: columns are the probabilities to leave the respective node.)
A. A random walker can never reach node A
B. A random walker can never leave node A
C. A random walker can always leave node C
D. A random walker can always leave node B
Answer: D
Details: A random walker can always leave node B | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following methods does not exploit statistics on the co-occurrence of words in a text?
A. Word embeddings
B. Transformers
C. Vector space retrieval
D. Fasttext
Answer: C
Details: Vector space retrieval
| epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which attribute gives the best split?A1PNa44b44A2PNx51y33A3PNt61j23
A. A1
B. A3
C. A2
D. All the same
Answer: B
Details: A3 | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Suppose that q is density reachable from p. The chain of points that ensure this relationship are {t,u,g,r} Which one is FALSE?
A. {t,u,g,r} have to be all core points.
B. p and q will also be density-connected
C. p has to be a core point
D. q has to be a border point
Answer: D
Details: q has to be a border point | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In User-Based Collaborative Filtering, which of the following is correct, assuming that all the ratings are positive?
A. Pearson Correlation Coefficient and Cosine Similarity have different value range, but return the same similarity ranking for the users
B. If the ratings of two users have both variance equal to 0, then their Cosine Similarity is maximized
C. Pearson Correlation Coefficient and Cosine Similarity have the same value range, but can return different similarity ranking for the users
D. If the variance of the ratings of one of the users is 0, then their Cosine Similarity is not computable
Answer: B
Details: If the ratings of two users have both variance equal to 0, then their Cosine Similarity is maximized | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: The term frequency of a term is normalized
A. by the maximal frequency of all terms in the document
B. by the maximal frequency of the term in the document collection
C. by the maximal frequency of any term in the vocabulary
D. by the maximal term frequency of any document in the collection
Answer: A
Details: by the maximal frequency of all terms in the document | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which is an appropriate method for fighting skewed distributions of class labels in classification?
A. Include an over-proportional number of samples from the larger class
B. Use leave-one-out cross validation
C. Construct the validation set such that the class label distribution approximately matches the global distribution of the class labels
D. Generate artificial data points for the most frequent classes
Answer: B,C
Details: Use leave-one-out cross validation.Construct the validation set such that the class label distribution approximately matches the global distribution of the class labels | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Thang, Jeremie and Tugrulcan have built their own search engines. For a query Q, they got precision scores of 0.6, 0.7, 0.8 respectively. Their F1 scores (calculated by same parameters) are same. Whose search engine has a higher recall on Q?
A. Thang
B. Jeremie
C. Tugrulcan
D. We need more information
Answer: A,C
Details: Thang.Tugrulcan | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: When compressing the adjacency list of a given URL, a reference list
A. Is chosen from neighboring URLs that can be reached in a small number of hops
B. May contain URLs not occurring in the adjacency list of the given URL
C. Lists all URLs not contained in the adjacency list of given URL
D. All of the above
Answer: B
Details: May contain URLs not occurring in the adjacency list of the given URL | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Data being classified as unstructured or structured depends on the:
A. Degree of abstraction
B. Level of human involvement
C. Type of physical storage
D. Amount of data
Answer: A
Details: Degree of abstraction | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Suppose you have a search engine that retrieves the top 100 documents and
achieves 90% precision and 20% recall. You modify the search engine to
retrieve the top 200 and mysteriously, the precision stays the same. Which one
is CORRECT?
A. The recall becomes 10%
B. The number of relevant documents is 450
C. The F-score stays the same
D. This is not possible
Answer: B
Details: The number of relevant documents is 450 | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In the χ2 statistics for a binary feature, we obtain P(χ2 | DF = 1) > 0.05. This means in this case, it is assumed:
A. That the class labels depends on the feature
B. That the class label is independent of the feature
C. That the class label correlates with the feature
D. None of the above
Answer: B
Details: That the class label is independent of the feature | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is correct regarding the use of Hidden Markov Models (HMMs) for entity recognition in text documents?
A. The cost of learning the model is quadratic in the lengths of the text.
B. The cost of predicting a word is linear in the lengths of the text preceding the word.
C. An HMM model can be built using words enhanced with morphological features as input.
D. The label of one word is predicted based on all the previous labels
Answer: C
Details: An HMM model can be built using words enhanced with morphological features as input. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: 10 itemsets out of 100 contain item A, of which 5 also contain B. The rule A -> B has:
A. 5% support and 10% confidence
B. 10% support and 50% confidence
C. 5% support and 50% confidence
D. 10% support and 10% confidence
Answer: C
Details: 5% support and 50% confidence | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is correct regarding the use of Hidden Markov Models (HMMs) for entity recognition in text documents?
A. HMMs cannot predict the label of a word that appears only in the test set
B. If the smoothing parameter λ is equal to 1, the emission probabilities for all the words in the test set will be equal
C. When computing the emission probabilities, a word can be replaced by a morphological feature (e.g., the number of uppercase first characters)
D. The label of one word is predicted based on all the previous labels
Answer: C
Details: When computing the emission probabilities, a word can be replaced by a morphological feature (e.g., the number of uppercase first characters) | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: A basic statement in RDF would be expressed in the relational data model by a table
A. with one attribute
B. with two attributes
C. with three attributes
D. cannot be expressed in the relational data model
Answer: B
Details: with two attributes | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following statements is wrong regarding RDF?
A. An RDF statement would be expressed in SQL as a tuple in a table
B. Blank nodes in RDF graphs correspond to the special value NULL in SQL
C. The object value of a type statement corresponds to a table name in SQL
D. RDF graphs can be encoded as SQL databases
Answer: B
Details: Blank nodes in RDF graphs correspond to the special value NULL in SQL | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: The number of non-zero entries in a column of a term-document matrix indicates:
A. how many terms of the vocabulary a document contains
B. how often a term of the vocabulary occurs in a document
C. how relevant a term is for a document
D. none of the other responses is correct
Answer: A
Details: how many terms of the vocabulary a document contains | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: What is TRUE regarding Fagin's algorithm?
A. Posting files need to be indexed by TF-IDF weights
B. It performs a complete scan over the posting files
C. It never reads more than (kn)1⁄2 entries from a posting list
D. It provably returns the k documents with the largest aggregate scores
Answer: D
Details: It provably returns the k documents with the largest aggregate scores | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: A false negative in sampling can only occur for itemsets with support smaller than
A. the threshold s
B. p*s
C. p*m
D. None of the above
Answer: D
Details: None of the above | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Why is XML a document model?
A. It supports application-specific markup
B. It supports domain-specific schemas
C. It has a serialized representation
D. It uses HTML tags
Answer: C
Details: It has a serialized representation | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: A retrieval model attempts to capture
A. the interface by which a user is accessing information
B. the importance a user gives to a piece of information for a query
C. the formal correctness of a query formulation by user
D. the structure by which a document is organised
Answer: B
Details: the importance a user gives to a piece of information for a query | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: When computing HITS, the initial values
A. Are set all to 1
B. Are set all to 1/n
C. Are set all to 1/sqrt(n)
D. Are chosen randomly
Answer: C
Details: Are set all to 1/sqrt(n) | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: When indexing a document collection using an inverted file, the main space requirement is implied by
A. The access structure
B. The vocabulary
C. The index file
D. The postings file
Answer: D
Details: The postings file | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: For a user that has not done any ratings, which method can make a prediction?
A. User-based collaborative RS
B. Item-based collaborative RS
C. Content-based RS
D. None of the above
Answer: ['none of the above'] | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In an FP tree, the leaf nodes are the ones with:
A. Lowest confidence
B. Lowest support
C. Least in the alphabetical order
D. None of the other options.
Answer: B,D
Details: Lowest support.None of the other options. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which statement is correct?
A. The Viterbi algorithm works because words are independent in a sentence
B. The Viterbi algorithm works because it is applied to an HMM model that makes an independence assumption on the word dependencies in sentences
C. The Viterbi algorithm works because it makes an independence assumption on the word dependencies in sentences
D. The Viterbi algorithm works because it is applied to an HMM model that captures independence of words in a sentence
Answer: B
Details: The Viterbi algorithm works because it is applied to an HMM model that makes an independence assumption on the word dependencies in sentences | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is WRONG about inverted files? (Slide 24,28 Week 3)
A. The space requirement for the postings file is O(n)
B. Variable length compression is used to reduce the size of the index file
C. The index file has space requirement of O(n^beta), where beta is about 1⁄2
D. Storing differences among word addresses reduces the size of the postings file
Answer: B
Details: Variable length compression is used to reduce the size of the index file | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In User-Based Collaborative Filtering, which of the following is TRUE?
A. Pearson Correlation Coefficient and Cosine Similarity have the same value range and return the same similarity ranking for the users.
B. Pearson Correlation Coefficient and Cosine Similarity have different value ranges and can return different similarity rankings for the users
C. Pearson Correlation Coefficient and Cosine Similarity have different value ranges, but return the same similarity ranking for the users
D. Pearson Correlation Coefficient and Cosine Similarity have the same value range but can return different similarity rankings for the users
Answer: B
Details: Pearson Correlation Coefficient and Cosine Similarity have different value ranges and can return different similarity rankings for the users | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is TRUE for Recommender Systems (RS)?
A. The complexity of the Content-based RS depends on the number of users
B. Item-based RS need not only the ratings but also the item features
C. Matrix Factorization is typically robust to the cold-start problem.
D. Matrix Factorization can predict a score for any user-item combination in the dataset.
Answer: D
Details: Matrix Factorization can predict a score for any user-item combination in the dataset. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following properties is part of the RDF Schema Language?
A. Description
B. Type
C. Predicate
D. Domain
Answer: D
Details: Domain | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is correct regarding crowdsourcing?
A. Uniform spammers randomly select answers.
B. The accuracy of majority voting is never equal to the one of Expectation Maximization.
C. Honey pots can detect uniform spammers, random spammers and sloppy workers.
D. Majority Decision and Expectation Maximization both give less weight to spammers’ answers.
Answer: C
Details: Honey pots can detect uniform spammers, random spammers and sloppy workers. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Given the 2-itemsets {1, 2}, {1, 3}, {1, 5}, {2, 3}, {2, 5}, when generating the 3-itemset we will:
A. Have 4 3-itemsets after the join and 4 3-itemsets after the prune
B. Have 4 3-itemsets after the join and 2 3-itemsets after the prune
C. Have 3 3-itemsets after the join and 3 3-itemsets after the prune
D. Have 2 3-itemsets after the join and 2 3-itemsets after the prune
Answer: B,C
Details: Have 4 3-itemsets after the join and 2 3-itemsets after the prune.Have 3 3-itemsets after the join and 3 3-itemsets after the prune | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: When using bootstrapping in Random Forests, the number of different data items used to construct a single tree is:
A. Smaller than the size of the training data set with high probability
B. Of order square root of the size of the training set with high probability
C. The same as the size of the training data set
D. Depends on the outcome of the sampling process, and can be both smaller or larger than the training set
Answer: A
Details: Smaller than the size of the training data set with high probability | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: To constrain an object of an RDF statement from being of an atomic type (e.g., String), one has to use the following RDF/RDFS property:
A. rdf:type
B. rdfs:range
C. rdfs:domain
D. rdfs:subClassOf
Answer: B
Details: rdfs:range | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: What is a correct pruning strategy for decision tree induction?
A. Apply Maximum Description Length principle
B. Stop partitioning a node when either positive or negative samples dominate the samples of the other class
C. Choose the model that maximizes L(M) + L(M|D)
D. Remove attributes with lowest information gain
Answer: B
Details: Stop partitioning a node when either positive or negative samples dominate the samples of the other class | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In the first pass over the database of the FP Growth algorithm
A. Frequent itemsets are extracted
B. A tree structure is constructed
C. The frequency of items is computed
D. Prefixes among itemsets are determined
Answer: C
Details: The frequency of items is computed | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In a FP tree, the leaf nodes are the ones with:
A. Lowest confidence
B. Lowest support
C. Least in the alphabetical order
D. None of the above
Answer: ['Lowest support\nNone of the above'] | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Your input is "Distributed Information Systems". Your model tries to predict "Distributed" and "Systems" by leveraging the fact that these words are in the neighborhood of "Information". This model can be:
A. Bag of Words
B. Word Embeddings
C. LDA
D. kNN
Answer: B
Details: Word Embeddings | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Considering the transaction below, which one is WRONG?
|Transaction ID |Items Bought|
|--|--|
|1|Tea|
|2|Tea, Yoghurt|
|3|Tea, Yoghurt, Kebap|
|4 |Kebap |
|5|Tea, Kebap|
A. {Yoghurt} -> {Kebab} has 50% confidence
B. {Yoghurt, Kebap} has 20% support
C. {Tea} has the highest support
D. {Yoghurt} has the lowest support among all itemsets
Answer: D
Details: {Yoghurt} has the lowest support among all itemsets | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which is an appropriate method for fighting skewed distributions of class labels in
classification?
A. Include an over-proportional number of samples from the larger class
B. Use leave-one-out cross validation
C. Construct the validation set such that the class label distribution approximately matches the global distribution of the class labels
D. Generate artificial data points for the most frequent classes
Answer: C
Details: Construct the validation set such that the class label distribution approximately matches the global distribution of the class labels | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Dude said “I like bowling”. With how many statements can we express this sentence using RDF Reification?
A. We cannot
B. 1
C. 3
D. 5
Answer: ['5.0'] | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Consider the following set of frequent 3-itemsets: {1, 2, 3}, {1, 2, 4}, {1, 2, 5}, {1, 3, 4}, {2, 3, 4}, {2, 3, 5}, {3, 4, 5}. Which one is not a candidate 4-itemset?
A. {1,2,4,5}
B. {1,3,4,5}
C. {2,3,4,5}
D. {1,2,3,4}
Answer: B
Details: {1,3,4,5} | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is true in the context of inverted files?
A. Index merging compresses an inverted file index on disk and reduces the storage cost
B. The trie structure used for index construction is also used as a data access structure to terms in the vocabulary
C. The finer the addressing granularity used in documents, the smaller the posting file becomes
D. Inverted files are optimized for supporting search on dynamic text collections
Answer: B
Details: The trie structure used for index construction is also used as a data access structure to terms in the vocabulary | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Regarding Label Propagation, which of the following is false?
A. The labels are inferred using the labels that are known apriori
B. It can be interpreted as a random walk model
C. Propagation of labels through high degree nodes are penalized by low abandoning probability
D. Injection probability should be higher when labels are obtained from experts than by crowdworkers
Answer: C
Details: Propagation of labels through high degree nodes are penalized by low abandoning probability | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: The type statement in RDF would be expressed in the relational data model by a table
A. with one attribute
B. with two attributes
C. with three attributes
D. cannot be expressed in the relational data model
Answer: A
Details: with one attribute | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Given graph 1→2, 1→3, 2→3, 3→2, switching from Page Rank to Teleporting PageRank will have an influence on the value(s) of:
A. All the nodes
B. Node 1
C. Node 2 and 3
D. No nodes. The values will stay unchanged.
Answer: A
Details: All the nodes | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is true regarding the random forest classification algorithm?
A. It is not suitable for parallelization.
B. It uses only a subset of features for learning in each weak learner.
C. We compute a prediction by randomly selecting the decision of one weak learner.
D. It produces a human interpretable model.
Answer: B
Details: It uses only a subset of features for learning in each weak learner. | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following properties is part of the RDF Schema Language?
A. Type
B. Predicate
C. Description
D. Domain
Answer: D
Details: Domain | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: How does matrix factorization address the issue of missing ratings?
A. It uses regularization of the rating matrix
B. It performs gradient descent only for existing ratings
C. It sets missing ratings to zero
D. It maps ratings into a lower-dimensional space
Answer: B
Details: It performs gradient descent only for existing ratings | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: When constructing a word embedding, negative samples are
A. word - context word combinations that are not occurring in the document collection
B. context words that are not part of the vocabulary of the document collection
C. all less frequent words that do not occur in the context of a given word
D. only words that never appear as context word
Answer: A
Details: word - context word combinations that are not occurring in the document collection | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following tasks would typically not be solved by clustering?
A. Community detection in social networks
B. Discretization of continuous features
C. Spam detection in an email system
D. Detection of latent topics in a document collection
Answer: C
Details: Spam detection in an email system | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In general, what is true regarding Fagin's algorithm?
A. It performs a complete scan over the posting files
B. It provably returns the k documents with the largest aggregate scores
C. Posting files need to be indexed by the TF-IDF weights
D. It never reads more than (kn)½ entries from a posting list
Answer: B
Details: It provably returns the k documents with the largest aggregate scores | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following statements is correct in the context of information extraction?
A. A confidence measure that prunes too permissive patterns discovered with bootstrapping can help reducing semantic drift
B. The bootstrapping technique requires a dataset where statements are labelled
C. Distant supervision typically uses low-complexity features only, due to the lack of training data
D. For supervised learning, sentences in which NER has detected no entities are used as negative samples
Answer: A
Details: A confidence measure that prunes too permissive patterns discovered with bootstrapping can help reducing semantic drift | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following statements on Latent Semantic Indexing (LSI) and Word Embeddings (WE) is correct?
A. LSI is deterministic (given the dimension), whereas WE is not
B. LSI does not take into account the order of words in the document, whereas WE does
C. The dimensions of LSI can be interpreted as concepts, whereas those of WE cannot
D. LSI does take into account the frequency of words in the documents, whereas WE does not
Answer: A,B,C
Details: LSI is deterministic (given the dimension), whereas WE is not.LSI does not take into account the order of words in the document, whereas WE does.The dimensions of LSI can be interpreted as concepts, whereas those of WE cannot | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: In vector space retrieval each row of the matrix M corresponds to
A. A document
B. A concept
C. A query
D. A term
Answer: D
Details: A term | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is correct regarding prediction models?
A. Training error being less than test error means overfitting
B. Training error being less than test error means underfitting
C. Complex models tend to overfit, unless we feed them with more data
D. Simple models have lower bias than complex models
Answer: C
Details: Complex models tend to overfit, unless we feed them with more data | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Applying SVD to a term-document matrix M. Each concept is represented in K
A. as a singular value
B. as a linear combination of terms of the vocabulary
C. as a linear combination of documents in the document collection
D. as a least squares approximation of the matrix M
Answer: B
Details: as a linear combination of terms of the vocabulary | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: An HMM model would not be an appropriate approach to identify
A. Named Entities
B. Part-of-Speech tags
C. Concepts
D. Word n-grams
Answer: D
Details: Word n-grams | epfl-collab |
Use this example only if it is relevant to answer precisely the question at the bottom.
Example :
Question: Which of the following is NOT an (instance-level) ontology?
A. Wordnet
B. WikiData
C. Schema.org
D. Google Knowledge Graph
Answer: C
Details: Schema.org | epfl-collab |
End of preview. Expand
in Data Studio
No dataset card yet
- Downloads last month
- 9