Data Analysis

An analyst runs an A/B test with 1000 users per group and observes a conversion rate of 5% in the control and 6% in the treatment. The p-value is 0.12. What should the analyst conclude?

Refer to the exhibit. Before running the code, the original salary column had 50 missing values. The median was calculated as 52000. After imputation, which of the following statements is true?

In A/B testing, the null hypothesis typically states that:

An analyst computed the mean, median, and mode of a dataset and found they are all equal. Which of the following best describes the distribution?

A time series of monthly sales data exhibits a clear upward trend over several years, with consistent peaks each December. Which components are present in this series?

A dataset contains a feature with values ranging from 10 to 1000. The analyst applies min-max normalization to scale the feature between 0 and 1. What is the normalized value of 520?

An analyst is comparing the average sales of two different store locations using a t-test. The p-value obtained is 0.03, and the significance level is 0.05. What should the analyst conclude?

A data analyst is preparing to run an A/B test comparing two email subject lines. Which TWO of the following should the analyst define before the test begins?

A retailer wants to test if a new website layout increases the average time spent on the site. They split traffic: control group (old layout) and treatment group (new layout). Which statistical test is most appropriate to compare the average time spent between the two groups?

A data analyst is performing a hypothesis test with a significance level of 0.05. The p-value obtained is 0.03. What should the analyst conclude?

A data analyst runs an A/B test on a new website layout. The test yields a p-value of 0.04 with the null hypothesis being no difference in conversion rates. The significance threshold is α=0.05. Which of the following is the correct conclusion?

Which TWO of the following are common assumptions of linear regression?

A data analyst is performing a chi-square test of independence on a 2x2 contingency table. The p-value is 0.04. At α=0.05, which THREE of the following statements are correct?

A dataset contains height measurements in centimeters and inches. An analyst wants to apply k-means clustering. Which data transformation should be applied before clustering?

A data analyst is testing whether a new website layout increases conversion rate. The p-value from the test is 0.03. Using a significance level of 0.05, what is the correct conclusion?

Which TWO of the following data quality dimensions are most directly affected by duplicate records?

A data scientist is analyzing a dataset with 100 variables and 5,000 records. The dataset has several missing values and a few extreme outliers. The goal is to build a regression model to predict a continuous target. Which combination of preprocessing steps is most likely to improve model performance?

In a dataset with variables on different scales (e.g., age in years and income in dollars), which preprocessing step is necessary before applying k-means clustering?

A data analyst at a retail company is building a multiple linear regression model to forecast weekly sales. The dataset contains 50 predictor variables, including store size, promotional spend, holiday indicators, and many others. After training the model, the analyst observes an R-squared of 0.99 on the training set but only 0.55 on the holdout test set. Which action should the analyst take first to address this discrepancy?

Which THREE of the following are appropriate methods to handle outliers in a dataset?

An analyst is preparing data for a clustering algorithm that uses Euclidean distance. Which TWO data preprocessing techniques should be applied to ensure all features contribute equally?

An analyst calculates a Pearson correlation coefficient of -0.8 between advertising spend and customer churn rate. Which interpretation is correct?

Match each data governance role to its responsibility.

A data analyst wants to segment customers into groups based on their purchasing behavior. The dataset includes numerical features such as annual income and purchase frequency. Which algorithm is most appropriate for this task?

Refer to the exhibit. Which data quality dimension is being violated?

A data analyst needs to determine whether the mean sales of two different regions are significantly different. The samples are independent and the data is normally distributed. Which statistical test should be used?

A data scientist is conducting an A/B test with a significance level of 0.05. Which three factors should be considered when calculating the required sample size? (Choose THREE)

A logistic regression model is used to predict the probability of customer churn. The model's coefficient for the feature 'customer support calls' is 0.8 with a p-value of 0.001. Which interpretation is correct?

In time series decomposition, a data analyst separates a retail sales series into trend, seasonal, and residual components. After decomposition, the residual component shows no pattern and is random. Which of the following best describes the seasonal component?

A data analyst is preparing a dataset for a predictive model. The dataset contains a feature 'age' with values ranging from 18 to 80, and a feature 'income' ranging from 20,000 to 200,000. To ensure both features contribute equally to distance-based algorithms, which transformation should the analyst apply?

A data analyst calculates the mean, median, and mode of a dataset. Which measure of central tendency is most affected by extreme outliers?

A retail company wants to identify customer segments based on purchase history and demographics. Which technique is most appropriate for this task?

A data scientist is using K-means clustering with k=3. After the first iteration, the centroids are recalculated. Which step occurs next in the algorithm?

In a multiple regression model, one predictor has a high p-value (0.45). What should the analyst consider doing?

A data analyst needs to join two tables in a SQL database: Orders and Customers. The analyst wants to include all orders, even if there is no matching customer record. Which type of join should be used?

A data analyst is examining the distribution of customer ages in a dataset. The ages are: 22, 25, 29, 30, 31, 34, 35, 37, 40, 42, 45, 50, 55, 60, 65. Which measure of central tendency would be least affected by an outlier if a value of 120 is incorrectly recorded as age 120?

In a multiple regression model with three predictors, the coefficient for one predictor is 5.2 with a p-value of 0.001. Which of the following is the best interpretation?

A company wants to determine if there is a significant difference in the average sales revenue between two different store layouts. They collect sales data from 30 stores with Layout A and 30 stores with Layout B. Which statistical test is most appropriate for comparing the means of these two independent groups?

A data scientist builds a logistic regression model to predict customer churn (yes/no). The model outputs a probability of 0.75 for a particular customer. Which of the following best describes this output?

A data analyst at a marketing firm is tasked with segmenting customers based on their purchasing behavior. The dataset contains 10,000 customers with features such as annual spend, frequency of purchases, recency of last purchase, and average order value. The analyst decides to use k-means clustering. After standardizing the features, the analyst runs k-means with k=3, k=4, and k=5, and computes the silhouette score for each: k=3: 0.45, k=4: 0.52, k=5: 0.48. The analyst also plots the elbow curve and observes that the within-cluster sum of squares (WCSS) decreases sharply from k=2 to k=4, then levels off. Based on these results, what is the most appropriate number of clusters?

A data analyst is evaluating data quality for a customer database. Which TWO dimensions of data quality are most directly affected by duplicate customer records?

A dataset contains the ages of 100 customers. The analyst wants to transform the ages to a 0-1 range for use in a distance-based algorithm. Which technique should be used?

A data analyst runs the Python code shown. What is the result of executing this code?

A data analyst is cleaning a dataset and finds missing values in a categorical variable representing customer region. Which imputation method is most appropriate?

An analyst is conducting an A/B test on a new website layout. Which TWO of the following must be defined before the test begins?

A marketing team runs an A/B test comparing two webpage designs. The null hypothesis states there is no difference in conversion rates. The p-value is 0.08 at α=0.05. Which is the correct interpretation?

Which measure best describes the spread of the middle 50% of a dataset?

An analyst uses K-means clustering on customer purchase data. After plotting the within-cluster sum of squares for different values of k, they observe an elbow at k=4. What is the most appropriate number of clusters?

A dataset has missing values in the 'age' column. The distribution of age is approximately normal with few outliers. Which imputation method is most appropriate?

A data analyst is comparing the average test scores of students who attended a tutoring program versus those who did not. Which statistical test is most appropriate for determining if there is a significant difference between the means of these two independent groups?

Match each data sampling method to its description.

In a linear regression model predicting house prices, the coefficient for the number of bedrooms is $30,000 and the intercept is $50,000. If a house has 3 bedrooms, what is the predicted price?

In time series analysis, which component represents regular patterns that repeat over fixed periods, such as daily or yearly?

After a marketing campaign, sales increased by 15%. The analyst wants to understand which customer segment contributed most to the increase. Which type of analysis is this?

A data scientist is building a K-means clustering model for customer segmentation. After plotting the within-cluster sum of squares (WCSS) against the number of clusters (k), she observes that the WCSS decreases sharply until k=5 and then levels off. Which value of k should she choose based on the elbow method?

Which TWO of the following are commonly used techniques for handling missing data in a dataset? (Select TWO).

A data analyst is preparing a dataset for a machine learning algorithm that assumes normally distributed features. Which TWO data transformation methods should the analyst consider to achieve this?

A dataset contains employee salaries ranging from $30,000 to $200,000. An analyst wants to scale the salaries to a range of 0 to 1 for use in a distance-based clustering algorithm. Which method should they use?

A data analyst needs to create a visual that shows the distribution of customer ages across different regions. Which chart type is most appropriate?

In an A/B test, the null hypothesis states that there is no difference between the conversion rates of the control and treatment groups. After collecting data, the p-value is 0.03. Using a significance level α = 0.05, what should the analyst conclude?

A data analyst is designing a data model for a sales data warehouse. The model should optimize query performance for aggregations by minimizing joins and duplicating data where necessary. Which schema design should the analyst use?

A data analyst uses linear regression to model the relationship between advertising spend and sales. The residual plot shows a clear U-shaped pattern. What assumption is violated?

A healthcare analytics team is building a predictive model to identify patients at high risk of readmission within 30 days of discharge. The dataset includes 50,000 patient records with 200 features, including demographics, vital signs, lab results, and historical admissions. The target variable is binary (readmitted or not). The team uses a logistic regression model and achieves an AUC of 0.72 on the test set. However, the model's calibration is poor: for patients predicted to have a 70% risk, the actual readmission rate is only 40%. The team wants to improve calibration without significantly reducing discrimination (AUC). The data scientist suggests applying Platt scaling. However, the team lead is concerned that Platt scaling may reduce the model's ability to rank patients correctly. Which of the following is the best course of action?

A data scientist is performing a hypothesis test with a significance level α=0.05. The p-value obtained is 0.03. What should the scientist conclude?

A healthcare analytics team is building a classification model to predict patient readmission within 30 days. The dataset contains 10,000 records with 30 features, including demographics, vital signs, lab results, and medication history. The target variable is imbalanced: 85% no readmission, 15% readmission. The team used logistic regression with default settings and achieved an accuracy of 85%, but the model predicted 'no readmission' for all patients. The lead analyst suspects the model is not learning due to class imbalance. The team has time to implement one corrective action before the next model review. Which action should the team take?

Which TWO of the following are components of time series data?

A data analyst wants to understand the relationship between advertising spend and sales revenue. The analyst calculates a Pearson correlation coefficient of 0.85. Which of the following is the best interpretation?

A company is analyzing customer feedback sentiment. The dataset is highly imbalanced with 95% positive and 5% negative comments. Which technique should the analyst use to address class imbalance before modeling?

Which TWO of the following are measures of central tendency?

Which THREE of the following are examples of descriptive statistics? (Select THREE.)

A data analyst wants to use a Z-score to standardize a dataset. The variable has a mean of 50 and a standard deviation of 10. What is the Z-score for a raw value of 70?

A data analyst notices that a dataset of customer ages has several missing values. Which method for handling missing data is most appropriate if the data is missing completely at random and the analyst wants to preserve sample size?

A data analyst is cleaning a dataset with missing values. Which TWO of the following are acceptable methods for handling missing numerical data?

A data analyst is building a logistic regression model to predict whether a customer will churn (yes/no). Which TWO statements about logistic regression are correct?

Which TWO of the following are valid techniques for validating the performance of a predictive model?

An analyst runs a simple linear regression with an R² value of 0.85. Which interpretation is correct?

An analyst is performing K-means clustering on customer data. The elbow method shows a clear bend at k=4. Which THREE of the following are true about K-means clustering with k=4?

A marketing analyst wants to predict whether a customer will churn (yes/no) based on account age and monthly charges. Which regression technique is most appropriate?

A data scientist is analyzing a dataset with multiple features and wants to apply k-means clustering to segment customers. She chooses k = 4 based on the elbow method. During the iteration process, which of the following correctly describes a step in the k-means algorithm?

Refer to the exhibit. Which type of ensemble method is being used?

A data analyst is reviewing a SQL query that joins three large tables. The query takes over an hour to run. The analyst notices that the WHERE clause filters on indexed columns in only two tables. Which of the following should the analyst do first to improve performance?

An analyst compares average sales across three different store locations using a statistical test. Which test is most appropriate?

A data analyst is evaluating the quality of a customer database. Which THREE of the following are dimensions of data quality?

Which data quality dimension ensures that data represents the real-world object or event correctly?

A data analyst is performing time series analysis on monthly sales data and notices a consistent pattern of higher sales every December. Which component of time series does this represent?

A data analyst is cleaning a dataset and identifies several outliers. Which TWO methods are appropriate for handling outliers?

A data analyst is examining sales data for a retail chain and notices that the mean monthly sales is $50,000 while the median is $35,000. Which of the following best describes the distribution of the sales data?

A data analyst is cleaning a dataset and finds that 5% of values in the 'income' column are missing. The analyst decides to impute missing values using the mean of the non-missing values. Which potential issue should the analyst be most concerned about?

A dataset contains a variable 'Income' with many missing values. The analyst decides to impute missing values with the median income of the non-missing values. Which type of imputation is this?

A data analyst needs to identify outliers in a dataset. Which of the following is a common method based on the interquartile range (IQR)?

Which THREE of the following are assumptions of linear regression? (Select THREE).

Which TWO of the following are examples of supervised learning algorithms?

A data analyst is examining the relationship between advertising spend (in dollars) and revenue (in dollars). The Pearson correlation coefficient r is calculated as +0.92. Which of the following interpretations is correct?

A data analyst needs to identify the most frequently occurring value in a dataset. Which measure of central tendency should they use?

A data analyst is performing a chi-square test of independence on a contingency table of customer satisfaction (satisfied vs. dissatisfied) and product type (A, B, C). The test yields a p-value of 0.04 with α = 0.05. What is the correct conclusion?

A company wants to segment its customers into distinct groups based on purchasing behavior. Which algorithm is best suited for this task?

A company runs an A/B test to compare a new website layout (treatment) against the current layout (control). The conversion rate for the control is 5% and for the treatment is 5.5%. The p-value is 0.06 at α=0.05. Which THREE of the following conclusions are valid?

A dataset contains features with vastly different scales (e.g., age 0-100 and income 0-1,000,000). Which data transformation should be applied before using a K-nearest neighbors algorithm?

A data analyst is preparing a dataset for analysis and needs to ensure data quality. Which TWO of the following are dimensions of data quality?

A data analyst wants to test if the proportion of customers who prefer Product A over Product B is different from 50%. She surveys 200 customers and finds that 120 prefer Product A. Which statistical test should she use?

A retail company wants to predict sales based on advertising spend and season. Which data modeling technique should the analyst use?

A data team is preparing data for a clustering analysis. Which THREE of the following steps are commonly part of data cleaning?

A data scientist is performing K-means clustering on customer data. She plots the within-cluster sum of squares (WCSS) for different values of k and observes an 'elbow' at k=4. What does this indicate?

A data analyst is evaluating a multiple regression model with three predictors. The R² value is 0.85. Which of the following is the best interpretation of R²?

Which TWO of the following are dimensional modeling techniques commonly used in data warehouses?

A data scientist trains a regression model and observes high variance with low bias. Which technique is most appropriate to reduce variance?

A logistic regression model predicts customer churn (0=no churn, 1=churn). The model outputs probabilities. Which THREE of the following statements about logistic regression are correct?

A data analyst is performing a chi-square test for independence between two categorical variables. Which THREE of the following are necessary conditions for the test to be valid?

A retail company wants to analyze monthly sales data over the past three years to identify long-term trends. Which component of time series analysis is most relevant for this goal?

A company is planning an A/B test to compare two website designs. Which THREE of the following must be determined before the test begins to ensure valid results? (Select three.)

A marketing analyst wants to segment customers based on their purchase history, including total spent, number of transactions, and average order value. The analyst runs k-means clustering with k=5 on the raw data but notices that the cluster assignments change significantly every time the algorithm is executed. What should the analyst do first to obtain consistent and meaningful clusters?

Refer to the exhibit. Which clause is used to aggregate the data by department?

You are a data analyst at a logistics company. The operations manager wants to reduce delivery delays. You have historical data including order date, delivery date, distance, weather conditions, and driver ID. Initial analysis shows that the average delivery time has increased over the past six months. You suspect that weather is a contributing factor, but you need to confirm. The company also wants to build a model to predict delivery times to better manage customer expectations. The data contains missing values for weather conditions in about 10% of records, and some driver IDs are incorrect. You have limited time and resources. What should you do first?

A data analyst is cleaning a dataset and finds that the 'age' column has several missing values. Which method of handling missing values is least likely to introduce bias if the missingness is completely at random?

A data analyst is comparing the means of two independent groups using a t-test. The sample sizes are small and the data is not normally distributed. Which condition is violated for a valid t-test?

A data analyst needs to summarize customer satisfaction scores. The data contains a few extremely low scores that skew the distribution. Which measure of central tendency is most appropriate?

A data analyst is performing data cleaning on a dataset and identifies several outliers in the 'age' column. Which TWO methods are appropriate for handling these outliers? (Select two.)

A data analyst is building a model to predict customer churn. The dataset has 10,000 records with 500 churned customers. The model predicts churn with 95% accuracy, but only identifies 10% of actual churners. Which metric best highlights this issue?

In an A/B test, the null hypothesis states that there is no difference between the control and treatment groups. After running the test, the p-value is 0.04. Assuming α = 0.05, what is the correct conclusion?

A dataset contains a column 'Income' with values in different scales (some in thousands, some in hundreds). What is the best way to standardize this column for use in a machine learning model?

After building a binary classification model, the data analyst obtains the following confusion matrix: True Positives=80, True Negatives=100, False Positives=20, False Negatives=30. What is the F1 score?

A data analyst is preparing to build a predictive model. Which TWO steps are essential to ensure model validity? (Choose two.)

A data analyst is preparing a dataset for analysis and needs to address data quality issues. Which TWO of the following are common data cleaning tasks?

A marketing team wants to segment customers into distinct groups based on purchasing behavior. The data includes numeric features such as frequency, monetary value, and recency. Which unsupervised learning algorithm should be used?

A data scientist is cleaning a dataset and notices missing values in several columns. Which THREE techniques are appropriate for handling missing data? (Select THREE.)

A data analyst is cleaning a customer dataset. Which two actions are appropriate for handling duplicate records? (Choose TWO)

A data analyst is preparing features for a machine learning model that uses distance-based algorithms (e.g., K-means, KNN). The dataset contains numerical features with different scales: age (0-100), income (20,000-200,000), and credit score (300-850). Which data transformation technique is most appropriate to ensure all features contribute equally to the distance calculations?

A data analyst is testing whether the average sales amount differs between two regions. Which statistical test is most appropriate?

A data scientist builds a simple linear regression model to predict house prices based on square footage. The model yields an R-squared value of 0.85. Which statement accurately interprets this result?

A data analyst is performing a chi-square test of independence on a contingency table of customer satisfaction (satisfied, neutral, dissatisfied) by region (North, South, East, West). Which THREE of the following are necessary assumptions for the test?

A dataset contains customer records with a column for 'Phone Number' that should be unique. However, the analyst finds several duplicate phone numbers. Which data quality dimension is primarily affected?

A marketing team runs an A/B test on email subject lines. The p-value is 0.03 with α = 0.05. Which of the following is the correct interpretation?

In logistic regression, the output is a probability between 0 and 1. If the predicted probability for a customer churning is 0.7 and the decision threshold is 0.5, what is the predicted class?

Drag and drop the steps to implement a data classification policy in the correct order.

Which TWO of the following are true about Pearson correlation coefficient (r)?

A data analyst is analyzing customer purchase amounts. The dataset contains several extreme high values due to luxury purchases. Which measure of central tendency is most robust to these outliers?

A financial analyst wants to compare the mean annual returns of three different investment strategies. Which statistical test is most appropriate?

A data analyst is preparing data for a k-nearest neighbors algorithm. The features include age (0-100) and income (0-200,000). Which technique should be applied to ensure the distance metric is not dominated by income?

A financial analyst is building a model to predict stock price movements. The data is time series with daily prices. The analyst wants to use a regression model but notices that the residuals are autocorrelated. What adjustment should be made?

A data analyst is working with a dataset that includes a column 'income' with values ranging from 20,000 to 150,000. To standardize this variable for a linear regression that assumes normally distributed residuals, which method should be used?

During ETL, a data analyst discovers that a date column contains values like '01/02/2023' and '2023-01-02'. Which of the following is the best practice to ensure consistent date format before analysis?

Which data cleaning method involves replacing a missing value with the average of the available values in that column?

A data analyst is building a linear regression model to predict sales based on advertising spend across TV, radio, and newspaper channels. Which TWO diagnostics should the analyst perform to validate the model assumptions?

A company has a dataset with 100 features. The data analyst wants to reduce dimensionality while preserving as much variance as possible. Which technique should be used?

A data analyst calculates a correlation coefficient of -0.85 between temperature and heating costs. What does this indicate?

Refer to the exhibit. An analyst runs the following query: SELECT product_id, AVG(quantity) FROM sales GROUP BY product_id HAVING AVG(quantity) > 8; Which product_id(s) will be returned?

A data analyst is performing a multiple linear regression with three predictors. The model output shows an R-squared of 0.85 and an adjusted R-squared of 0.80. Which of the following is the best interpretation of the difference between these two values?

A marketing team uses K-means clustering to segment customers based on purchase history. To determine the optimal number of clusters, they plot the within-cluster sum of squares (WCSS) against k and look for an elbow. What is the purpose of this method?

After training a decision tree, the tree has depth 20 and 100% accuracy on training data but only 60% on test data. Which hyperparameter adjustment is most likely to improve generalization?

An analyst wants to compare the mean sales revenue across three different store regions. The data is normally distributed and variances are equal. Which statistical test is most appropriate?

A researcher is designing an A/B test to compare two website layouts. Which TWO elements are essential for determining the required sample size?

Refer to the exhibit. A data analyst wants to grant read access to an entire cloud storage bucket named 'data-lake'. Which of the following best describes what this policy does?

A data analyst is cleaning a dataset and finds that some records have duplicate entries based on customer ID. Which data quality dimension is most directly affected by these duplicates?

A data analyst is building a supervised learning model to predict customer churn. The target variable is binary (churn = yes/no). Which TWO modeling techniques are appropriate for this task? (Select two.)

In time series decomposition, a pattern that repeats at regular intervals (e.g., weekly, yearly) is called:

A simple linear regression model predicts sales (y) from advertising spend (x). The equation is y = 2.5x + 10, and R² = 0.81. Which interpretation is correct?

In a time series analysis, a retail analyst observes consistent peaks in sales every December and troughs every February. This pattern repeats annually. Which component of time series does this represent?

A dataset contains outliers in a feature that will be used for linear regression. Which two outlier treatment methods are appropriate? (Choose TWO)

A data scientist is tuning a decision tree model to prevent overfitting. The model currently has a high variance. Which hyperparameter adjustment is most effective?

A marketing analyst wants to segment customers based on purchasing behavior and demographics. The dataset includes continuous variables (spending amount, frequency) and categorical variables (region, gender). The analyst decides to use k-means clustering. What should the analyst do to prepare the data?

A data analyst wants to predict customer churn based on categorical features like region and plan type, and continuous features like usage and tenure. Which regression type should be used?

During data exploration, an analyst notices that the target variable has a heavily right-skewed distribution. Which data transformation would be most appropriate to make the distribution more symmetric?

A company’s marketing team wants to segment customers based on purchase history, demographics, and website behavior. The data includes both numeric and categorical variables. Which clustering algorithm is best suited for handling mixed data types?

A data analyst is working with a dataset containing house prices. After building a multiple linear regression model, the analyst observes that the model performs well on training data but poorly on validation data. Which technique is most appropriate to address this issue?

A data analyst is preparing a logistic regression model to predict customer churn. After examining the exhibit, which data quality issue should the analyst address first?

A dataset contains a feature 'Age' with values ranging from 18 to 95. To prepare data for a k-nearest neighbors algorithm, which transformation should be applied to 'Age'?

A data analyst wants to compare the average revenue per customer between two marketing campaigns (A and B). The analyst is unsure if the data follows a normal distribution. Which statistical test is most appropriate for comparing the means of the two groups?

A data analyst has a time series of monthly sales data. They observe that sales are consistently higher every December and lower every January. Which component of time series does this pattern represent?

Given the linear regression output, which independent variable has the strongest effect on price, based on standardized coefficients?

An analyst is conducting an A/B test to compare two website designs. The null hypothesis is that there is no difference in conversion rates. The p-value obtained is 0.03, and the significance threshold is 0.05. What should the analyst conclude?

A data analyst calculates the mean, median, and mode of a dataset. Which of the following best describes how these measures are used in descriptive statistics?

A stock analyst is analyzing monthly sales data for a retail company and observes a consistent pattern of high sales every December. This pattern is most likely an example of which time series component?

In simple linear regression, the coefficient of determination R² measures:

In a regression analysis, the coefficient of determination (R²) is 0.85. How should this value be interpreted?

A data analyst is building a binary classification model to predict customer churn. The dataset is imbalanced, with only 10% churners. The analyst wants to evaluate model performance with a focus on correctly identifying churners. Which metric is most appropriate?

A retail company wants to predict future sales based on historical data. Which modeling approach is most appropriate if the data shows a clear seasonal pattern?

A retail company wants to segment its customers based on purchase history. Which THREE methods are appropriate for customer segmentation?

The exhibit shows an SQL query executed on an 'orders' table that contains 'order_id', 'customer_id', and 'order_date'. What is the purpose of this query?

An analyst is preparing data for an A/B test and wants to ensure valid results. Which TWO of the following should be considered when calculating the required sample size?

A data analyst wants to compare the means of three different training methods on employee productivity. Which statistical test is most appropriate?

An analyst is planning an A/B test to compare two website designs. Which TWO factors should be considered when calculating the required sample size?

A data analyst is cleaning a dataset with missing values in a time series of daily temperatures. The missing values occur sporadically. Which imputation method is most appropriate to maintain the temporal trend?

A data analyst is reviewing a dataset containing house prices. The mean price is $350,000 and the median is $280,000. Which of the following best describes the distribution of house prices?

A retail company wants to forecast monthly sales for the next 12 months. Sales data shows a clear upward trend and seasonal patterns that repeat yearly. Which time series model is most appropriate?

In multiple linear regression, which TWO assumptions are critical for unbiased coefficient estimates? (Choose two.)

Which TWO of the following are appropriate uses of min-max normalisation?

Which data quality dimension ensures that data represents the real-world scenario correctly and without errors?

A data analyst is analyzing survey responses where respondents rated satisfaction on a scale of 1-5. The analyst wants to visualize the distribution of responses. Which chart type is most appropriate?

In A/B testing, which factor is increased by having a larger sample size?

Which THREE of the following are common steps in data cleaning?

Which data quality dimension is violated if a customer record has a missing phone number?

A data scientist is building a classification model to predict customer churn. The dataset has 10,000 records with 500 churners. The scientist uses logistic regression and achieves 98% accuracy, but the precision for churn class is only 15%. Which of the following is the most likely cause?

An analyst wants to compare the average sales revenue across three different store locations. Which TWO statistical methods are appropriate for this comparison?

A marketing team wants to segment customers into groups based on purchasing behavior without prior labels. Which algorithm should the data analyst use?

A data scientist is working with a dataset containing 1000 features and 500 samples. The goal is to build a predictive model. Which technique should be used to reduce the number of features while retaining most of the variance?

A data analyst needs to combine two datasets that have the same columns but different rows. Which operation should they use?

In a simple linear regression model y = 2.5 + 1.2x, what is the predicted value of y when x = 10?

A data analyst wants to segment customers based on purchasing behavior such as frequency, monetary value, and recency. Which TWO clustering evaluation methods can help determine the optimal number of clusters? (Select two.)

A data analyst is asked to compare the average sales across three different store locations. The data is normally distributed and variances are approximately equal. Which statistical test is most appropriate?

An analyst is performing a logistic regression to predict customer churn (yes/no). The model outputs a probability of 0.75 for a particular customer. Which of the following best describes the interpretation?

An analyst is conducting an A/B test on a new checkout process. To calculate sample size, which THREE factors must be considered?

A data analyst needs to visualize the distribution of a continuous variable across different categories. Which chart type is most suitable?

A data analyst is performing data cleaning. Which THREE steps are part of this process? (Choose three.)

Which statistical test should be used to determine if there is a significant association between two categorical variables, such as gender and product preference?

A data scientist runs a linear regression model to predict customer spending based on income. The R-squared value is 0.45 and the p-value for the slope coefficient is 0.03. At a significance level of α=0.05, which of the following conclusions is correct?

A data analyst is building a linear regression model to predict sales based on advertising spend. The analyst notices that the residuals are not normally distributed and have a non‑constant variance. Which of the following transformations is most appropriate to apply to the dependent variable?

Which data quality dimension is most concerned with whether data values fall within a defined domain or acceptable range?

A data scientist applies K-means clustering to a customer dataset. The elbow method suggests using 4 clusters. After running K-means with k=4, the within-cluster sum of squares (WCSS) is plotted against k, and the elbow is at k=4. What does this indicate?

A data analyst is examining the relationship between advertising spend (in thousands) and sales (in thousands). The Pearson correlation coefficient is computed as r = -0.85. Which of the following interpretations is correct?

A data analyst trains a complex model that achieves 99% accuracy on training data but only 65% on new data. What is the most likely issue?

A data analyst is cleaning a dataset and finds that the 'age' column has several missing values. Which of the following is a valid method for handling missing numerical data?

A data analyst is summarizing the central tendency of a dataset with extreme outliers. Which measure is most robust to outliers?

A data analyst is cleaning a dataset and finds that a numeric field has several missing values. The variable is normally distributed. Which imputation method is most appropriate?

A data analyst calculates the mean, median, and mode of a dataset. Which of the following measures of central tendency is least affected by extreme outliers?

A data analyst uses the elbow method to determine the number of clusters for k-means. The plot shows a sharp bend at k=3 and a small bend at k=5. What is the recommended number of clusters?

A data analyst is performing hypothesis testing to compare the mean sales of two store locations. Which TWO conditions must be satisfied to use a two‑sample t‑test? (Select TWO.)

An analyst is performing a linear regression and obtains an R-squared value of 0.85. Which of the following is the best interpretation?

A data scientist is building a model to predict customer churn (yes/no). After training a logistic regression model, the coefficient for 'monthly charges' is 0.05 with a p-value of 0.03. Which interpretation is correct at α=0.05?

Drag and drop the steps to normalize a database table from 1NF to 3NF in the correct order.

A data analyst is performing K-means clustering on customer data. Which THREE of the following are steps in the K-means algorithm?

A dataset contains a column 'Age' with values: [22, 25, 25, 30, 35, 40, 45]. What is the interquartile range (IQR)?

A data analyst is conducting an A/B test on a website's landing page. The null hypothesis is that there is no difference in conversion rates between the control and treatment groups. After collecting data, the analyst calculates a p-value of 0.03. Using a significance level of α = 0.05, what is the correct conclusion?

Which TWO of the following are true about correlation and causation? (Select TWO).

A data scientist is preparing data for a K-means clustering algorithm. The dataset contains features measured in different units (e.g., income in dollars and age in years). Which preprocessing step is most critical before running K-means?

An analyst is fitting a polynomial regression model and wants to choose the degree that minimizes overfitting. Which technique should the analyst use?

A data scientist is building a predictive model to forecast monthly sales. The data shows a linear trend with no seasonality. Which regression technique is most appropriate?

In a logistic regression model predicting customer churn (1 = churn, 0 = not churn), the coefficient for 'contract length' is -0.5. Which of the following is the correct interpretation?

A data analyst is preparing a dataset for analysis and needs to handle outliers. Which TWO of the following are common methods for treating outliers?

A healthcare analytics team is analyzing patient readmission rates. They have a dataset with thousands of records including patient age, diagnosis, length of stay, number of prior admissions, and discharge date. The goal is to identify key factors influencing readmission and create a model to predict high-risk patients. The data is imbalanced: only 5% of patients are readmitted within 30 days. The team plans to use logistic regression. What is the most appropriate approach?

A data analyst calculates the mean, median, and mode of a sales dataset and finds they are all equal. Which type of distribution does this indicate?