DA0-002 Practice Test 4 — 10 Questions

Question 1

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?

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

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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 10

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