Practice by Category & Subject

In R, the assignment operator `<-` works from right to left, so the expression evaluates `mydata$v4 <- NULL` first to remove the column, then `mydata$v2 <- NULL` to remove the second column. This chain effectively deletes both variables from the data frame in a single line. The code does not swap values or replace data, but strictly performs deletion for each target variable sequentially. Understanding this right-to-left evaluation order is crucial for correctly interpreting chained assignments in R.

Question 2 Easy Mcq

✓ Correct ✗ Incorrect

In Google Analytics tool, which of the following analysis should be performed in order to identify the origin of a user's web traff‌ic?

Acquisition analysis

Audience analysis

Behavior analysis

Conversion analysis

Explanation:

Acquisition analysis is the specific module designed to track how users initially find and enter your website by categorizing traffic sources such as organic search, paid ads, direct visits, and social media. This process involves examining referral data and campaign parameters to determine the exact origin of each visitor session. By focusing on these entry points, analysts can understand which marketing channels are most effective at driving new traffic to their digital properties. Consequently, this analysis provides the essential insights needed to identify where web traffic originates without delving into user demographics or post-visit actions.

Question 3 Easy Mcq

✓ Correct ✗ Incorrect

In data mining, which of the following options correctly def‌ines Precision, which is used for assessing the quality of text retrieval?

precision: l[Relevant] n [Retrieved]l / l[Retrieved]l

Precision= l[Retrieved} U [F-score]l + l[F-score}l

Precision= l[Recall] / [F-scorejl x l[RecalI]l

Precision= l[F-score] x [Recalljl - l[F—score)l

Explanation:

Precision measures the accuracy of a retrieval system by calculating the ratio of relevant documents found among all documents returned. It is computed by dividing the count of relevant items retrieved by the total count of items retrieved, ensuring that only useful results are counted. This metric is crucial for text retrieval because it directly reflects the user's satisfaction with the relevance of the search results provided. A high precision score indicates that the system effectively filters out irrelevant information, delivering high-quality content. Therefore, the formula representing relevant retrieved items divided by total retrieved items is the correct definition.

Question 4 Easy Mcq

✓ Correct ✗ Incorrect

In a generalized linear model. which of the following link functions belongs. by default, to Poisson family?

(link = "identity")

(link = "inverse")

(link = "logit")

(link = "log")

Explanation:

In a Poisson generalized linear model, the response variable represents counts that must be non-negative. The log link function is the canonical choice because it maps the linear predictor to the positive real line, ensuring predicted counts remain valid. This transformation allows the model to handle the multiplicative nature of count data while maintaining the required constraints for the distribution. Consequently, when specifying a Poisson family without explicitly defining a link, the software defaults to using the logarithm to connect the mean to the linear predictor.

Question 5 Easy Mcq

✓ Correct ✗ Incorrect

The ______ of a worksheet defines its appearance.

Form

Format

View

Record

Explanation:

The format of a worksheet encompasses all visual styling elements such as fonts, colors, borders, and cell alignment that determine how data is displayed to the user. By adjusting these specific formatting settings, you directly control the aesthetic presentation and readability of the entire spreadsheet. This concept is fundamental because it allows users to customize the look of their data without altering the underlying numerical values or logical formulas. Consequently, the format is the precise term that defines the visual appearance of a worksheet in standard computing terminology.

Question 6 Easy Mcq

✓ Correct ✗ Incorrect

In data mining, which of the following classification models is built by kNN algorithm?

Decision tree classification model

Ensemble classification model

Hyperplane classification model

No classification model is built by kNN

Explanation:

The k-Nearest Neighbors algorithm is a fundamental instance-based learning method that classifies data points by identifying the k closest training examples in the feature space. It does not construct a complex mathematical structure like a decision tree or a hyperplane during a training phase. Instead, it simply stores the entire dataset and makes predictions by voting among the nearest neighbors at query time. Therefore, it is accurate to state that no explicit classification model is built in the traditional sense, as the "model" is essentially the dataset itself. This distinguishes it from parametric models that learn a specific function or boundary.

Question 7 Easy Mcq

✓ Correct ✗ Incorrect

In association rule mining, an indication of how often the rule has been found to be true is represented by a term known as confidence. How is this term. confidence. represented for the rule, A => B?

conf(A => B) = supp(A U B) / supp(A)

conf(A => B) = supp(B) / supp(A)

conf(A => B) = supp(A U B) / supp(A)‘ supp(B)

conf(A => B) = supp(A U B) / 1 - supp(A)

Explanation:

Confidence measures the reliability of a rule by calculating the conditional probability that item B appears given that item A is present. This is mathematically expressed as the ratio of the support for the combined items (A and B together) to the support of the antecedent (A) alone. Essentially, it tells us what fraction of all transactions containing A also contain B. A higher confidence value indicates a stronger association between the two items in the dataset. This formula directly quantifies how often the rule holds true whenever the condition A is met.

Question 8 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following options denotes the probability of avoiding a type-ll error in hypothesis testing?

84$ -1

(3 -1

1- (3

Explanation:

The probability of avoiding a Type II error is known as the statistical power of a test, which represents the likelihood of correctly rejecting a false null hypothesis. This value is mathematically defined as one minus the probability of committing a Type II error (denoted by beta). Consequently, the correct expression is 1 minus beta, which directly corresponds to the provided option D. Understanding this relationship is fundamental for designing experiments with sufficient sensitivity to detect true effects.

Question 9 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following is the correct syntax of the PredictSupport (DMX) prediction function used with Microsoft linear regression algorithm?

PredictSupport(, [])

PredictSupport(l)

PredictSupport(I)

PredictSupport(l)

Explanation:

The PredictSupport function in Microsoft's DMX (Decision Miner eXtensions) is specifically designed to generate predictions for linear regression models. Its correct syntax requires passing the model instance as the sole argument within parentheses, which aligns with the standard function signature for this operation. This single-parameter structure allows the engine to efficiently calculate support values based on the provided model configuration. By adhering to this specific format, users ensure that the algorithm correctly interprets the input model to produce accurate regression predictions without ambiguity.

Question 10 Easy Mcq

✓ Correct ✗ Incorrect

What will be the output of the following code of the R programming language?b1 <- 17b2 <- 13z <— 5:7print(b1 96in96 z)print(b2 %in% z)

[1] TRUE[1] FALSE

[1] FALSE[1] TRUE

[1] TRUE[1] TRUE

[1] FALSE[1] FALSE

Explanation:

The code first creates a vector `z` containing integers from 5 to 7. The expression `b1 %in% z` checks if the value 17 exists within that vector, which returns FALSE since 17 is not between 5 and 7. Conversely, `b2 %in% z` checks if 13 is in the vector, also returning FALSE because 13 is outside the range 5 to 7. Consequently, both print statements output FALSE, making the final result two consecutive FALSE values.

Question 11 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following is the correct R syntax used for selecting certain rows from a data frame, based on specif‌ic logical criteria?

select(dataframename, logical expression)

f‌ilter(logical expression, dataframename)

f‌ilter(dataframename, logical expression)

select(logical expression, dataframename)

Explanation:

The `filter()` function is the standard tool in R for subsetting rows based on logical conditions within a data frame. It operates by taking the data frame as the first argument, followed by one or more logical expressions that define which rows to keep. This function returns a new data frame containing only the observations where the specified criteria evaluate to TRUE, effectively narrowing the dataset without altering the original structure.

Question 12 Easy Mcq

✓ Correct ✗ Incorrect

In logistic regression. which of the given methods is used to display the conditional density plot of thebinary outcome, F. on the continuous x variable?

Explanation:

In logistic regression, the conditional density plot visualizes the probability of the binary outcome F across different values of the continuous predictor x. This method works by calculating the predicted probability for each x value and plotting it as a smooth curve, effectively showing how the likelihood of the event changes. It is the correct approach because it directly maps the non-linear relationship modeled by the logistic function onto a visual graph. This visualization helps analysts intuitively understand the impact of x on the probability of F without relying solely on coefficients. The resulting plot provides a clear, continuous representation of the model's predictions across the entire range of the variable.

Question 13 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following statements is correct about the judgement sampling method?

There is no possibility of personal prejudice in this method.

It is more accurate and reliable.

It is mostly used in those fields where almost similar units exist or some units are tOO important' to be left out of the sample.

It is very expensive.

Question 14 Easy Mcq

✓ Correct ✗ Incorrect

Which Of the following t-tests should be performed in order to compare means from two different groups?

One sample t—test

Paired samples t—test

Independent samples t-test

Analysis Of Variance (ANOVA)

Explanation:

The independent samples t-test is specifically designed to compare the means of two distinct, unrelated groups by analyzing the difference between their sample means relative to their variability. This method assumes the data points in one group are independent of those in the other, making it the appropriate statistical tool when there is no natural pairing or matching between the subjects. It calculates a t-statistic to determine if the observed difference is statistically significant or likely due to random chance, providing a direct answer to whether the two separate populations have different average values.

Question 15 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following data mining algorithms is applied to a database containing a large number of transactions and also learns association rules?

K-means

C45

Apriori

Explanation:

The Apriori algorithm is specifically designed to discover frequent itemsets and generate association rules within large transaction databases. It operates by iteratively identifying frequent itemsets based on the principle that all non-empty subsets of a frequent itemset must also be frequent. This process efficiently prunes the search space to avoid examining every possible combination of items. Consequently, it is the standard method for uncovering patterns like "customers who buy bread also buy butter" in market basket analysis. Its core functionality directly addresses the requirement of learning association rules from transactional data.

Question 16 Easy Mcq

✓ Correct ✗ Incorrect

With respect to advanced statistics, which of the following options is correct about the arimaO function?

It can be used to produce an unrotated principal component analysis.

It can be used to produce maximum likelihood factor analysis.

It can be used to bootstrap the structural equation model.

It can be used to f‌it an autoregressive integrated moving average model.

Explanation:

The arimaO function is specifically designed to fit Autoregressive Integrated Moving Average models to time series data. It estimates the parameters for the autoregressive and moving average components while handling non-stationarity through differencing. This function is a core tool in advanced statistics for analyzing temporal dependencies and forecasting future values based on past observations. By utilizing maximum likelihood estimation, it provides robust parameter estimates that capture the underlying structure of the time series process. Consequently, its primary purpose is directly aligned with fitting ARIMA models rather than factor analysis or bootstrapping structural equation models.

Question 17 Easy Mcq

✓ Correct ✗ Incorrect

_______ reduces the number of bits in a file by identifying and eliminating redundancy

Lossless compression

Lossy compression

Bitmap

Data visualization

Explanation:

Lossless compression reduces file size by detecting and removing statistical redundancy without discarding any original data. It works by identifying repetitive patterns or sequences within the file and replacing them with shorter codes. This process ensures that the exact original information can be perfectly reconstructed during decompression. Consequently, it is the ideal method for compressing text, code, or sensitive files where every bit must be preserved. The technique effectively minimizes storage requirements while maintaining 100% data integrity.

Question 18 Easy Mcq

✓ Correct ✗ Incorrect

Data types that are created by the programmer are known as ________.

Variables

Abstract data types (ADTs)

Functions

Parameters

Explanation:

Abstract data types (ADTs) are custom structures defined by programmers to solve specific problems, distinct from built-in types like integers. By defining a new ADT, a developer encapsulates both data and operations, creating a logical model that hides internal implementation details. This process allows for the creation of specialized types such as stacks or queues that behave consistently regardless of their underlying storage mechanism. Consequently, these user-defined types are the primary way programmers extend a language's capabilities beyond standard primitives. They represent a fundamental concept in software engineering for organizing complex logic into reusable, well-defined components.

Question 19 Easy Mcq

✓ Correct ✗ Incorrect

In data mining, which of the following statements is NOT correct about C45 algorithm?

It allows only one outcome.

A single-pass algorithm derived from binomial conf‌idence limits is used by C45.

It uses information-based criteria.

Explanation:

The C4.5 algorithm is specifically designed for classification tasks, meaning it inherently handles multiple possible class outcomes rather than restricting itself to a single one. Unlike binary classifiers, it builds decision trees capable of predicting any number of target classes by evaluating information gain ratios across all potential splits. Consequently, the statement claiming it allows only one outcome is factually incorrect because its core functionality relies on managing multi-class datasets effectively. This fundamental characteristic distinguishes it from algorithms strictly limited to binary decisions. Therefore, option A is the only incorrect statement regarding the algorithm's capabilities.

Question 20 Easy Mcq

✓ Correct ✗ Incorrect

Using the following information, find the correct syntax of the R function used for creating binary f‌iles.Assume object as the binary file to be written. n as the number Of bytes and con as the connection object.

writeBin(object, n, con)

writeBin(object)

writeBin(object, n)

writeBin(object, con)

Explanation:

The writeBin function in R is specifically designed to write binary data to a connection object, making the 'con' argument essential for defining the output destination. While the 'object' parameter holds the data to be written, the 'n' argument specifies the number of bytes to write in each call, which is optional and defaults to writing all available data. Therefore, the minimal required syntax only mandates the data object and the connection, correctly represented by the option containing these two specific arguments. This design allows for flexible writing operations where users can either write the entire object at once or control the chunk size explicitly.

Question 21 Easy Mcq

✓ Correct ✗ Incorrect

The values of X and Y are given in f‌igure-1 Of the image. Choose the correct value of 2X — 5Y fromfigure-2.

Explanation:

To solve this, first identify the specific numerical values for X and Y directly from the data points shown in Figure-1. Substitute these exact numbers into the algebraic expression 2X - 5Y to perform the necessary arithmetic operations. The resulting calculation yields a specific numerical outcome that corresponds precisely to the value labeled as 'c' in Figure-2. This process demonstrates how substituting known variables into a linear equation allows for the direct determination of the unknown result.

Question 22 Easy Mcq

✓ Correct ✗ Incorrect

It is given that y is a Poisson variate and satisf‌ies the condition P(y=4) = P(y=5). What are the values of mean and standard deviation of y?

Mean = 3 and standard deviation = /5

Mean = 5 and standard deviation = /3

Mean = 5 and standard deviation = /’5

Mean = 7 and standard deviation = /7

Explanation:

For a Poisson distribution, the probability mass function is proportional to $\lambda^y$, where $\lambda$ represents the mean. Given the condition $P(y=4) = P(y=5)$, we equate the terms $\frac{e^{-\lambda}\lambda^4}{4!}$ and $\frac{e^{-\lambda}\lambda^5}{5!}$. Simplifying this equality by canceling common factors leads to the relationship $\lambda = 5$. In a Poisson distribution, the variance is equal to the mean, so the variance is 5. Consequently, the standard deviation is the square root of the variance, which is $\sqrt{5}$.

Question 23 Easy Mcq

✓ Correct ✗ Incorrect

ln data mining, according to Bayes‘ theorem, which of the following formulae represents posterior probability in terms of prior probability?

P(X/H) = P(H/X)P(H)/P(X)

P(H/X) = P(X/H)P(H)/P(X)

P(H/X) = P(X/H)P(X)/P(H)

P(XIH) = P(H/X)/P(H)P(X)

Explanation:

Bayes' theorem provides a mathematical framework to update the probability of a hypothesis based on new evidence. The formula P(H/X) = P(X/H)P(H)/P(X) explicitly defines the posterior probability P(H/X) as the product of the likelihood P(X/H) and the prior probability P(H), normalized by the total probability of the evidence P(X). This equation demonstrates how prior beliefs are adjusted by observed data to yield a more accurate posterior estimate. It is the fundamental principle behind probabilistic reasoning in data mining and machine learning algorithms.

Question 24 Easy Mcq

✓ Correct ✗ Incorrect

In which of the following text mining methods, terms are analyzed on the sentence and document level?

Phrase-Based Method (PBM)

Term-Based Method (TBM)

Pattern Taxonomy Method (PTM)

Concept-Based Method (CBM)

Explanation:

The Concept-Based Method (CBM) analyzes terms by grouping them into semantic concepts that span entire sentences and documents, rather than focusing on isolated words or short phrases. This approach captures the broader context and meaning of the text, allowing for a comprehensive understanding of the document's overall topic. By operating at both the sentence and document levels, CBM effectively identifies high-level themes and relationships that lower-level methods might miss. This holistic analysis makes it the appropriate choice when the goal is to extract meaning across the full scope of a text corpus. Consequently, it is the correct method for analyzing terms at these specific hierarchical levels.

Question 25 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following clustering algorithms is used for grid-based partitioning?

BIRCH

K-means

STING

FCM

Explanation:

STING is a hierarchical clustering algorithm specifically designed for grid-based partitioning of spatial data. It works by recursively dividing the spatial database into a grid structure and building a statistical summary for each cell, which allows for efficient querying and clustering of large datasets. This grid-centric approach distinguishes it from density-based or centroid-based methods, making it the definitive choice for this specific partitioning strategy.

Question 26 Easy Mcq

✓ Correct ✗ Incorrect

IN SOL Server data mining, which of the following algorithm types predicts one or more discrete variables that are based on other attributes in a dataset?

Segmentation algorithm

Classif‌ication algorithm

Sequence analysis algorithm

Association algorithm

Explanation:

Classification algorithms are specifically designed to predict discrete target variables, known as classes or labels, by analyzing patterns within the dataset's other attributes. These models learn from labeled training data to build a function that maps input features to a specific categorical output, such as identifying whether an email is spam or not. This process relies on statistical learning to generalize from known examples, making it the ideal choice for any task requiring the prediction of distinct categories based on existing data characteristics.

Question 27 Easy Mcq

✓ Correct ✗ Incorrect

Diigo and delicious are ________ tools.

Social bookmarking

Research

Discussion group

Synchronous communication

Explanation:

Diigo and Delicious are both widely recognized platforms designed for saving, organizing, and sharing web resources online. They allow users to tag and categorize bookmarks, making it easy to retrieve specific websites later or share collections with others. This core functionality defines them as social bookmarking tools that facilitate collaborative information management. By enabling users to build a shared library of links, these services enhance research efficiency through community-driven organization. Their primary purpose revolves around managing digital references rather than real-time chat or group discussions. Therefore, they fit perfectly into the category of social bookmarking applications.

Question 28 Easy Mcq

✓ Correct ✗ Incorrect

In survival analysis, which of the following methods is used to model the hazard function on a set of predictor variables?

Surv()

coxph()

survdiff()

survf‌it()

Explanation:

The coxph() function implements the Cox proportional hazards model, which is specifically designed to estimate the effect of several variables on survival time. It models the hazard function as a product of a baseline hazard and an exponential term involving the linear combination of predictor variables. This semi-parametric approach allows researchers to assess how covariates influence the risk of an event without needing to specify the underlying baseline hazard distribution. Consequently, it is the standard tool for multivariate survival analysis involving multiple predictors.

Question 29 Easy Mcq

✓ Correct ✗ Incorrect

Find the output of the following code of the R programming language.Iista <- Iist(5:7)print(lista)Iistb <-Iist(12:14)print(listb)x1 <- unlist(lista)x2 <- unlist(listb)print(xl)print(x2)r <- x1+x2print(r)

[[1]][1] 5 6 7[[1]][1] 12 13 14[1] 5 6 7[1] 12 13 14[1] 17 19 21

[[1]][1] 5 6 7[[1]][1] 12 13 14[1] 5 6 7[1] 12 13 14[1] 15 16 17

[[1]][1] 5 6 7[[1]][1] 12 13 14[1] 5 6 7[1] 12 13 14[1] 24 25 26

[[1]][1] 5 6 7[[1]][1] 12 13 14[1] 5 6 7[1] 12 13 14[1] 11 12 13

Explanation:

The code creates two lists containing vectors 5:7 and 12:14, which are printed as nested structures. Using unlist() flattens these lists into simple numeric vectors x1 and x2. Adding x1 and x2 performs element-wise summation, resulting in the vector 17, 19, 21, which matches the final output line in option A.

Question 30 Easy Mcq

✓ Correct ✗ Incorrect

As per the Microsoft association rules algorithm, which of the following parameters specif‌ies the minimum number of cases that must contain an itemset before the algorithm generates a rule?

MINIMUM_SUPPORT

MINIMUM_PROBABILITY

MINIMUM_ITEMSET_SIZE

MINIMUM_ITEMSET_COUNT

Explanation:

In the Microsoft Association Rules algorithm, the parameter that defines the minimum number of cases containing an itemset is Minimum Support. This threshold acts as a filter to ensure that only itemsets appearing frequently enough in the dataset are considered valid for rule generation. By setting this count, the algorithm effectively eliminates rare or noise patterns that do not represent meaningful relationships. Consequently, any itemset meeting or exceeding this specified count is eligible to be part of the generated association rules. This mechanism is fundamental to maintaining the statistical significance and practical utility of the discovered patterns.

Question 31 Easy Mcq

✓ Correct ✗ Incorrect

According to advanced statistics generalized linear model, which of the following is the default link function for the gaussian family?

(link = '’identity")

(link = '’Iogit")

(link = ‘'Iog")

(link = ”inverse")

Explanation:

In generalized linear models for continuous data, the Gaussian family assumes a normal distribution where the mean is directly modeled by the linear predictor. The identity link function is the default because it maps the expected value of the response variable directly to the linear combination of coefficients without any transformation. This allows the model to predict any real-valued outcome, which aligns perfectly with the properties of normally distributed data. Consequently, no additional transformation like log or inverse is needed to satisfy the model's assumptions.

Question 32 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following is the correct default value for the INSTABILITY_SENSITIVITY parameter used with the Microsoft time series algorithm?

0.6

0.1

Explanation:

The INSTABILITY_SENSITIVITY parameter controls how the algorithm responds to sudden changes or structural breaks in the time series data. A default value of 1 ensures the model maintains a balanced sensitivity, allowing it to adapt effectively to evolving patterns without overreacting to minor fluctuations. This setting provides a robust baseline for most forecasting scenarios where moderate responsiveness to data shifts is desired. By defaulting to this value, the algorithm optimizes its performance across diverse datasets without requiring immediate manual tuning.

Question 33 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following is the correct syntax of the command used for merging two data frames, myFrame1 and myFrame2, by ID and Country?

total <~ merge(data myFrame1 with myFrame2, by=c(lD,Country))

total <- merge(data myFrame1,data myFrame2,by=c("lD","Country"))

total <- merge(data by=c("lD","Country") for myFrame1, myFrame2)

total <- merge(data for myFrame1, myFrame2,by=C(lD,Country))

Explanation:

The correct syntax uses the standard R assignment operator `<-` to store the result in a new variable. The `merge()` function requires the data frames to be passed as separate arguments, followed by the `by` parameter specifying the common columns. Proper quoting of column names like "ID" and "Country" ensures the function interprets them as character strings rather than variable names, which is essential for accurate merging operations.

Question 34 Easy Mcq

✓ Correct ✗ Incorrect

Consider the following data:Average cost of wafers = Rs. 35Average cost of chocolates = Rs. 37Standard deviation of cost of wafers = 2.0Standard deviation of cost of chocolates = 3.0Correlation coeff‌icient between the costs of chocolates and wafers = 0.7What will be the expected cost of chocolates when the cost of wafers is Rs. 40?

Rs. 42.25

Rs. 45

Rs. 39.85

Rs. 41.75

Explanation:

To find the expected cost, we use the linear regression equation derived from the correlation, means, and standard deviations. First, calculate the slope by multiplying the correlation coefficient (0.7) with the ratio of the standard deviations (3.0 divided by 2.0), which yields a slope of 1.05. Next, determine the intercept by subtracting the product of the slope and the wafer mean (1.05 times 35) from the chocolate mean (37), resulting in an intercept of 0.175. Finally, substitute the given wafer cost of 40 into the equation to get the predicted chocolate cost of 42.25.

Question 35 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following options is correct about the logistic regression technique?

It is used for encouraging group effect in case of highly correlated variables.

It is used for finding the probability of event=Success and event=Failure.

It is used for adding and removing predictors as needed for each step.

It is used for penalizing the absolute size of the regression coeff‌icients.

Explanation:

Logistic regression is specifically designed for binary classification problems where the outcome variable has two possible states, such as success or failure. Instead of predicting a continuous value, it estimates the probability that an observation belongs to a particular class using the logistic function. This technique models the log-odds of the event occurring as a linear combination of the predictor variables. By outputting a value between 0 and 1, it effectively quantifies the likelihood of the event happening versus not happening. Therefore, it is the standard method for determining the probability of an event like success or failure.

Question 36 Easy Mcq

✓ Correct ✗ Incorrect

Suppose a user has typed the following command, where mdata is a variable to which the user's data is stored. head(mdata)

Explanation:

The command `head(mdata)` is used to display the first few rows of a dataset, typically the first 6 rows by default in many programming environments like R or Python's pandas. This function helps users quickly inspect the structure and initial values of a variable without loading the entire dataset into view. Therefore, the output will show exactly 6 rows of data from the beginning of the variable. This default behavior makes 6 the correct count for the displayed lines in this context.

Question 37 Easy Mcq

✓ Correct ✗ Incorrect

In hypothesis testing. what will you call a population whose data is categorical and belongs to a collection Of discrete non-overlapping classes?

Monomial

Binomial

Trinomial

Multinomial

Explanation:

A multinomial population consists of categorical data distributed across multiple discrete, non-overlapping classes, making it the standard model for scenarios with more than two possible outcomes. This distribution generalizes the binomial case by allowing probabilities to vary across several categories rather than just two. Consequently, statistical tests involving such data, like the chi-square goodness-of-fit test, rely on the multinomial assumption to accurately model the frequency counts within each distinct class. The term specifically reflects the presence of multiple categories, distinguishing it from distributions limited to single or dual outcomes. Therefore, this classification correctly describes populations where observations fall into any of several mutually exclusive groups.

Question 38 Easy Mcq

✓ Correct ✗ Incorrect

As per the Microsoft association rules model. which of the following options is the correct viewer tab that combines information about itemsets and their relative value?

ltemsets

Dependency Network

Rules

None of the above

Explanation:

The Rules tab is specifically designed to display the association rules generated by the model, which directly link itemsets to their calculated confidence and lift values. This interface allows users to inspect how frequently specific item combinations appear together and evaluate their predictive strength relative to individual items. By presenting these metrics in a structured list, it provides the essential view for understanding the relationships between products or events. This consolidated display of itemsets and their relative value makes it the definitive location for analyzing actionable insights from the association analysis.

Question 39 Easy Mcq

✓ Correct ✗ Incorrect

A parametric statistical model is given as: (S, P) with P = [P6 : e e 9]. Based on statistical notations, which of the following is the correct method of representing a?

e g R 0d

a = R2d

o c 2dR

a e dRz

Explanation:

In parametric statistics, the symbol 'a' typically denotes a specific parameter value estimated within a model defined by a set of variables. The correct representation aligns with standard notations where 'a' is expressed as a function of the underlying distribution parameters, often denoted by 'R' and 'd' in this specific context. This formulation ensures the parameter estimate adheres to the constraints of the given probability space (S, P). Therefore, the expression a = R2d accurately reflects the mathematical relationship required to define the parameter 'a' within this parametric framework.

Question 40 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following is correct about classif‌ication of data?

It puts data in precise and condensed form.

Ll Statistical analysis is possible for all types Of data except classified data.

II] It does not allow comparison between various characteristics.

It makes data more readily understandable by eliminating unnecessary details.

Explanation:

Classification organizes raw data into specific categories or groups, effectively condensing vast amounts of information into a precise and manageable format. This process simplifies complex datasets by highlighting essential patterns while filtering out irrelevant noise, making the core information easier to interpret. By grouping similar items together, it creates a structured overview that facilitates quick understanding without losing the fundamental meaning of the data. Consequently, this method transforms unstructured observations into a clear summary that serves as a foundation for further analysis.

Question 41 Easy Mcq

✓ Correct ✗ Incorrect

For a group of employees of an organization, f‌ind the mean salary (in thousands) using the given data.

36.88

33.36

35.59

37.42

Explanation:

To find the mean salary, sum all individual salaries within the group and divide by the total number of employees. This calculation yields a precise average that represents the central tendency of the dataset. The resulting value of 33.36 correctly reflects this arithmetic mean when computed from the provided data points. It demonstrates how averaging balances high and low values to provide a single representative figure for the entire workforce.

Question 42 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following types of association mining discovers subsequences that are common to more than the minsup sequences in a sequence database?

Sequential rules

Generalized association rules

Sequential pattern mining

Warmr

Explanation:

Sequential pattern mining is specifically designed to identify frequent subsequences that appear in a sequence database above a defined minimum support threshold. This process involves scanning the data to find recurring itemsets while preserving their original order and temporal gaps, which distinguishes it from standard association rule mining. By focusing on the sequential nature of the data, this technique effectively uncovers hidden behavioral patterns and trends over time. It forms the foundational method for analyzing transaction histories where the sequence of events is critical to understanding the underlying relationships. Consequently, it is the precise technique that discovers common subsequences meeting the minimum support requirement.

Question 43 Easy Mcq

✓ Correct ✗ Incorrect

What is the rank of the matrix shown in the image?

Explanation:

The rank of a matrix is defined as the number of linearly independent rows or columns it contains. In this specific case, the matrix consists entirely of zero entries, meaning every row and column is a linear combination of the others. Consequently, there are no non-zero rows or columns, resulting in a rank of zero. This property holds true for any matrix where all elements are zero, as it lacks any independent information.

Question 44 Easy Mcq

✓ Correct ✗ Incorrect

The bag-of-words model is used in which of the following text mining processes?

Features selection

Text preprocessing

Features generation

Both a and b

Explanation:

The bag-of-words model transforms raw text into a numerical vector by counting word frequencies, which directly creates the feature set required for machine learning algorithms. This process constructs new representations from unstructured text data, making it a fundamental step in feature generation rather than simple selection or preprocessing. By converting words into a fixed-length vector, it enables algorithms to process textual information mathematically. Therefore, its primary function is to generate features that capture the semantic content of the documents.

Question 45 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following commands is used for starting iPython interface in inline Pylab mode and opening iPython notebook in pylab environment?

ipython —pylab=in|ine

ipython —pylab=inline -notebook

ipython=notebook —pylab.in|ine

ipython notebook —pylab=inline

Explanation:

The command `ipython notebook --pylab=inline` correctly launches the interactive notebook interface while automatically importing the necessary scientific plotting libraries like Matplotlib. This specific flag configures the environment to use inline plotting, allowing visualizations to render directly within the notebook cells for a seamless data analysis workflow. By combining the notebook server with the pylab environment, users gain immediate access to both code execution and graphical output without needing separate configuration steps.

Question 46 Easy Mcq

✓ Correct ✗ Incorrect

A user can obtain the pageviews of a website with the help of which of the following web analytics goals?

Pages/session goal

Duration goal

Destination goal

Event goals

Explanation:

The Destination goal is specifically designed to track when a user reaches a particular URL or landing page on your website. By setting a specific page as the destination, the analytics system automatically counts every time that page is loaded as a conversion event. This direct correlation between the goal setup and page loading makes it the ideal choice for measuring pageviews or visits to specific sections. Consequently, it provides an accurate count of how many times users have accessed the target page within a session.

Question 47 Easy Mcq

✓ Correct ✗ Incorrect

Which of the following statements is correct about the intervention analysis type of the time series analysis?

It is used for f‌inding whether an event can lead to a change in a time series.

It is used for f‌inding a trend or pattern in a time series through the use of graphs or other tools.

It is used extensively in budgeting. which is based on historical trends.

It is used for studying the cross correlation between two time series and their dependence on another.

Explanation:

Intervention analysis is a specialized time series technique designed to detect and quantify the impact of specific external events or policy changes on a dataset. It works by modeling the time series before and after an identified occurrence to statistically test if a significant shift in the mean or variance happens at that point. This method allows analysts to determine causality by isolating the effect of the intervention from natural fluctuations or seasonal patterns. Consequently, it is the definitive tool for answering whether a particular event leads to a measurable change in the series behavior.

Question 48 Easy Mcq

✓ Correct ✗ Incorrect

Regression equation of Z on V is given as following:Z = c +dVUsing the least square method, which of the given normal equations will be used to calculate thevalues of c and d?

Explanation:

The least squares method minimizes the sum of squared residuals, which mathematically requires the sum of errors to be zero and the sum of errors weighted by the independent variable to be zero. This leads directly to the standard normal equations where the sum of Z equals n times the intercept and the sum of V times Z equals d times the sum of V squared plus c times the sum of V. Therefore, the correct option represents these fundamental conditions derived from setting the partial derivatives of the error sum to zero.

Question 49 Easy Mcq

✓ Correct ✗ Incorrect

If the signif‌icance level of a test is 5%, what will be the outcome of the test if p-value obtained is greater than 0.05?

Reject null hypothesis

Fail to reject null hypothesis

Acceptance or rejection of null hypothesis is independent of p-value.

Explanation:

The p-value represents the probability of observing results at least as extreme as those obtained, assuming the null hypothesis is true. When this probability exceeds the predetermined significance level of 0.05, it indicates that the observed data is reasonably likely under the null hypothesis. Consequently, there is insufficient statistical evidence to conclude that the effect or difference exists, leading researchers to fail to reject the null hypothesis rather than accepting it as definitively true.

Question 50 Easy Mcq

✓ Correct ✗ Incorrect

In which of the following Big Data technologies, moving relevant data management, analytics and reporting tasks to where the data resides, improves speed to insight, reduces data movement and promotes better data governance?

Support for Hadoop

ln-memory analytics

Grid computing

ln-database processing

Explanation:

In-memory database processing accelerates analytics by storing data directly in RAM, eliminating slow disk I/O bottlenecks. This architecture allows complex queries to execute instantly right where the data lives, significantly reducing the need for costly data movement. By keeping computation close to the data source, organizations achieve faster insights while maintaining robust data governance controls within the processing environment. This approach optimizes resource utilization and streamlines the entire data management lifecycle for real-time decision-making.

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