DA0-002 Practice Test 2 — 25 Questions

Question 1

A data analyst is preparing a presentation for executive leadership. The analyst wants to highlight the correlation between marketing spend and revenue over the past year. Which visualization type is most appropriate for showing this relationship?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Scatter plot. A scatter plot is the most appropriate visualization for showing the relationship between two continuous variables—marketing spend and revenue—because it plots individual data points on an X-Y axis, allowing the analyst to visually assess correlation, trends, and outliers. This directly supports the goal of highlighting correlation, as the pattern of points (e.g., upward slope) indicates the strength and direction of the relationship.

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Pie chart

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Bar chart

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Histogram

Question 2

Drag and drop the steps to clean a dataset with missing values in the correct order.

Accepted Answer

Step 1: Identify missing values, Step 2: Analyze missingness pattern, Step 3: Select imputation/removal strategy, Step 4: Apply the strategy, Step 5: Document changes. Cleaning starts with identification, then analysis of missingness, strategy selection, application, and documentation.

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Step 1: Analyze missingness pattern, Step 2: Identify missing values, Step 3: Select imputation/removal strategy, Step 4: Apply the strategy, Step 5: Document changes

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Step 1: Identify missing values, Step 2: Select imputation/removal strategy, Step 3: Analyze missingness pattern, Step 4: Apply the strategy, Step 5: Document changes

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Step 1: Identify missing values, Step 2: Analyze missingness pattern, Step 3: Apply the strategy, Step 4: Document changes, Step 5: Select imputation/removal strategy

Question 3

In pandas, you have a DataFrame 'df' with columns 'product' and 'sales'. You want to calculate the total sales per product. Which method should you use?

Accepted Answer

df.groupby('product')['sales'].sum(). The groupby('product')['sales'].sum() pattern is the canonical pandas idiom for split-apply-combine aggregation: it groups rows by the 'product' column, selects the 'sales' Series, and applies the sum aggregator to each group, returning a Series indexed by product. This is the most direct and efficient way to compute total sales per product.

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df['sales'].apply(sum)

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df.pivot_table(values='sales', index='product', aggfunc='sum')

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df.merge(df, on='product')

Question 4

A data analyst at a subscription media company is asked to determine whether a new recommendation engine increased average watch time. The analyst plans a two-sample t-test comparing a randomly assigned control group against a randomly assigned treatment group, with watch time recorded in minutes per user per week. Which TWO conditions must hold for the pooled-variance two-sample t-test to be statistically valid here? (Choose two.)

Accepted Answer

The population variances must be equal, or the sample sizes must be large enough for the test's robustness.. The pooled two-sample t-test rests on independent observations and an assumption about variance equality that is relaxed by large, balanced samples. Random assignment to control and treatment satisfies independence, and checking variance similarity or group balance safeguards the test's nominal error rate. Sample-size thresholds and measurement-scale restrictions are not assumptions of this procedure, so they do not belong in the analyst's validation checklist.

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Both groups must contain at least 30,000 users to satisfy the central limit theorem.

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Watch time must be measured on a ratio scale with a true zero and no upper bound.

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The control and treatment groups must have identical means before the experiment begins.

Question 5

A data analyst needs to visualize sales per capita across U.S. states. States with small populations but high sales (e.g., Delaware) appear too prominent on a choropleth map. Which technique best addresses this issue?

Accepted Answer

Use a choropleth map with rates instead of raw sales. Using a choropleth map with rates (e.g., sales per capita) instead of raw sales normalizes the data by population, preventing states with small populations from appearing overly prominent. This addresses the issue where Delaware, with high sales but low population, dominates the map. Rates allow for fair comparison across states regardless of population size.

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Switch to a bar chart sorted by sales

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Use a bubble chart with size proportional to sales

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Apply a log scale to the color gradient

Question 6

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?

Accepted Answer

Normalize the features and set a fixed random seed for the initial centroids.. The instability in cluster assignments is caused by the algorithm's sensitivity to the scale of features and the random initialization of centroids. Normalizing the features ensures that each variable contributes equally to the distance calculations, while setting a fixed random seed makes the initial centroid selection deterministic, leading to reproducible results.

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Switch to hierarchical clustering, which does not require specifying k.

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Increase the number of clusters to k=10 to capture more detail.

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Use principal component analysis (PCA) to reduce the number of features to two.

Question 7

A data analyst needs to present findings about customer churn to business stakeholders. The analysis identified that churn is highest among customers who have called customer support more than three times in the last month. Which of the following is the best way to communicate this insight?

Accepted Answer

A bar chart comparing churn rates for different support call counts.. A bar chart directly compares churn rates across discrete categories of support call counts (e.g., 0, 1, 2, 3, 4+ calls), making it easy for stakeholders to see the spike at 'more than three calls'. This aligns with the insight that churn is highest among customers with >3 support calls, and a bar chart is the standard visualization for comparing a continuous metric (churn rate) across categorical bins.

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A scatter plot to show the relationship between support calls and churn.

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A pie chart showing the proportion of churned vs. retained customers.

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A table of raw churn data by customer ID.

Question 8

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

Accepted Answer

Seasonality. Seasonality refers to a pattern that repeats at fixed, calendar-based intervals such as weekly, monthly, quarterly, or yearly. Because the question specifies 'regular intervals' like weekly or yearly, this is the defining characteristic of the seasonal component in time series decomposition. Seasonality is distinct from trend (long-term direction) and cyclical (irregular multi-year fluctuations).

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Cyclical

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Irregular

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Trend

Question 9

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

Accepted Answer

Apply Ridge regularization. Ridge regularization (L2) reduces variance by adding a penalty term proportional to the square of the coefficients, which shrinks them toward zero without eliminating them. This directly addresses high variance (overfitting) by constraining the model's complexity, while low bias indicates the model fits the training data well. The regularization parameter λ controls the trade-off between bias and variance.

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Increase polynomial features

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Use a smaller training set

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Remove correlated features

Question 10

Which THREE of the following are properties of ratio data? (Choose THREE.)

Accepted Answer

Supports multiplication and division. Ratio data supports multiplication and division because it has a true, meaningful zero point that indicates the absence of the measured attribute. This allows ratios to be computed (e.g., one value is twice another), which is a defining property of ratio scales in measurement theory.

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Data can be categorized into groups

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Allows negative values

Question 11

An analyst is reviewing a table that stores customer orders. The table contains columns: OrderID, CustomerName, Product1, Product1Qty, Product2, Product2Qty. This design violates which normal form?

Accepted Answer

First normal form (1NF). The table violates First Normal Form (1NF) because it contains repeating groups (Product1, Product1Qty, Product2, Product2Qty) instead of storing each product in a separate row. 1NF requires that each column contains atomic values and that there are no repeating groups or arrays. The presence of multiple product columns for a single order breaks this atomicity and normalization rule.

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No violation

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Third normal form (3NF)

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Second normal form (2NF)

Question 12

A company is designing a data pipeline to process streaming data from social media feeds. Which THREE of the following are characteristics of streaming data? (Select THREE).

Accepted Answer

Data is unbounded and infinite. Streaming data is inherently unbounded and infinite because social media feeds generate a continuous, never-ending flow of events. Unlike batch data, there is no natural end to the stream; new tweets, posts, or interactions arrive constantly, making the dataset theoretically infinite in size.

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Data is processed in micro-batches

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Data is stored permanently before processing

Question 13

A data analyst is using a public API to collect historical weather data. The API has a rate limit of 100 requests per minute, but the analyst needs to retrieve 10,000 records as quickly as possible. What strategy should be used?

Accepted Answer

Paginate with appropriate delays. Paginating with appropriate delays respects the API's rate limit of 100 requests per minute while maximizing throughput. By splitting the 10,000 records into pages (e.g., 100 records per page) and sending requests at a rate just under the limit (e.g., one request every 0.6 seconds), the analyst can retrieve all data in approximately 100 minutes without triggering HTTP 429 rate-limit errors.

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Increase the request rate

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Use multiple API keys

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Download a precompiled dataset

Question 14

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?

Accepted Answer

Apply Platt scaling on a held-out validation set to recalibrate the predicted probabilities without refitting the original model.. Platt scaling is a post-processing technique that fits a logistic regression model on the predicted probabilities from the original model using a held-out validation set. This recalibrates the probabilities without altering the ranking of patients (the AUC remains unchanged), directly addressing the poor calibration while preserving discrimination. Option C correctly describes this procedure.

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Remove poorly calibrated predictions by discarding all patients with predicted risk between 0.3 and 0.7.

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Ignore calibration because AUC is the only metric that matters for readmission risk models.

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Switch to a random forest model, which inherently produces better-calibrated probabilities.

Question 15

A data analyst is preparing a report on customer satisfaction scores. To comply with GDPR, which THREE actions must be taken? (Select THREE.)

Accepted Answer

Ensure aggregates do not identify individuals. Option B is correct because GDPR's data minimization and purpose-limitation principles require that aggregated statistics used for reporting must not allow re-identification of any individual, so ensuring aggregates do not identify individuals protects data subjects' privacy. Option D is correct because anonymizing personally identifiable information (PII) such as names, email addresses, and account numbers removes the personal data from scope of GDPR processing, satisfying the regulation's requirement to protect identifiable data. Option E is correct because GDPR Article 5(1)(e) mandates storage limitation, meaning the analyst must establish defined data retention periods for the report data rather than keeping it longer than necessary. Option A is incorrect because retaining data indefinitely violates the GDPR storage-limitation principle. Option C is incorrect because including customer names for context unnecessarily introduces identifiable personal data, contradicting data minimization and the anonymization requirement.

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Retain data indefinitely for analysis

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Include customer names for context

Question 16

A data analyst is using a recursive CTE to traverse an organizational hierarchy. What is the purpose of the anchor member in the recursive CTE?

Accepted Answer

It provides the initial seed or starting rows for the recursion.. The anchor member initializes the recursion with the base result set.

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It filters the final output of the recursive CTE.

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It specifies how to join the CTE with itself recursively.

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It defines the termination condition for the recursion.

Question 17

A company uses an OLTP system for processing customer transactions. Which characteristic is most important for this system to ensure that each transaction is processed reliably, even if multiple users access the system simultaneously?

Accepted Answer

It follows ACID properties. ACID properties (Atomicity, Consistency, Isolation, Durability) are the foundational guarantees that ensure each transaction in an OLTP system is processed reliably, even when multiple users access the system concurrently. Atomicity ensures all-or-nothing execution, Consistency preserves database invariants, Isolation prevents concurrent transactions from interfering with each other, and Durability guarantees committed transactions survive failures. These properties directly address the requirement for reliable transaction processing under simultaneous access.

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It uses a columnar storage format

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It stores data in a denormalized schema

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It supports complex analytical queries

Question 18

Which database index type is most commonly used for exact-match lookups and range queries in a B-tree structure?

Accepted Answer

B-tree index. A B-tree index is the correct answer because it maintains sorted data in a balanced tree structure, enabling both exact-match lookups (via equality searches) and efficient range queries (via ordered traversal of leaf nodes). This dual capability makes it the standard index type in relational databases like MySQL, PostgreSQL, and Oracle for general-purpose querying.

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Hash index

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Clustered index

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Bitmap index

Question 19

An e-commerce company wants to analyze sales performance across product categories. The dataset includes transaction amounts and a column 'category' with values (Electronics, Clothing, Home). The analyst decides to use stratified sampling to ensure proportional representation. Which THREE steps are required to implement this? (Select THREE).

Accepted Answer

Calculate the proportion of each category in the population. Stratified sampling requires first partitioning the population into homogeneous subgroups, so option C is correct: dividing the dataset into three strata based on the 'category' column (Electronics, Clothing, Home) creates the strata. Next, option A is correct because the analyst must calculate each category's proportion of the total population to determine how many samples each stratum should contribute for proportional representation. Option B is also correct because, after determining stratum sizes, a random sample must be drawn from each stratum with size proportional to its population proportion, which is the defining allocation step of proportional stratified sampling. Option D is incorrect because selecting every 10th transaction describes systematic sampling, not stratified sampling, and it ignores the category strata. Option E is incorrect because merging all categories into one group and performing simple random sampling removes the stratification and does not guarantee proportional representation across categories.

Answer

Select every 10th transaction from the entire dataset

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Combine all categories into a single group and perform simple random sampling

Question 20

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?

Accepted Answer

Apply SMOTE to oversample the readmission class. SMOTE (Synthetic Minority Oversampling Technique) directly addresses the class imbalance by generating synthetic samples for the minority class (readmission). This forces the logistic regression model to learn decision boundaries that separate the two classes, rather than defaulting to the majority class prediction. With 85% majority and 15% minority, accuracy alone is misleading, and SMOTE is a proven technique to improve recall for the minority class.

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Remove features with low variance to reduce noise

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Use accuracy as the evaluation metric to monitor improvement

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Switch to a random forest model with default settings