DA0-002 Hard Questions — 25 Difficult Questions

Question 1

During an ETL process, a data quality check fails due to duplicate customer IDs. Which data quality dimension is violated?

Accepted Answer

Uniqueness. Duplicate customer IDs violate the uniqueness dimension because uniqueness ensures that each record in a dataset has a distinct identifier with no duplicates. In an ETL process, a primary key or unique constraint on the customer ID column would reject duplicate values, causing the data quality check to fail. This is distinct from consistency, which checks for logical agreement across data sources.

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Consistency

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Completeness

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Accuracy

Question 2

An analyst creates a dashboard with a large amount of decorative graphics and background images. According to CompTIA Data+, which design principle is being violated?

Accepted Answer

Data-ink ratio. The data-ink ratio, a concept from Edward Tufte, states that a visualization should maximize the proportion of ink used to represent actual data relative to total ink used. By adding excessive decorative graphics and background images, the analyst reduces this ratio, making the dashboard less efficient and potentially obscuring the data. CompTIA Data+ emphasizes minimizing non-data ink to improve clarity and insight extraction.

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Visual hierarchy

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Appropriate precision

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Consistent color coding

Question 3

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

Accepted Answer

Ridge regression (L2). Ridge regression (L2) adds a penalty proportional to the square of the magnitude of coefficients, which shrinks them toward zero but does not eliminate them. This regularization reduces variance and helps prevent overfitting in polynomial regression by controlling the influence of higher-degree terms, making it the correct technique for minimizing overfitting while retaining all features.

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Lasso regression (L1)

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Principal component analysis (PCA)

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Stepwise selection

Question 4

Refer to the exhibit. An analyst sees this log during data acquisition. What action should be taken first?

Accepted Answer

Modify the ETL mapping for data types. The log shows a data type mismatch during ETL (Extract, Transform, Load) processing, where a date field is being read as a string. The correct first action is to modify the ETL mapping for data types to ensure the date string is properly cast or converted to the target date format, preventing data loss or corruption. This aligns with standard data acquisition best practices: adjust the transformation layer to handle source data anomalies before rejecting or altering the source.

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Reject the entire dataset

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Ignore warnings and continue

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Correct the date string in the source

Question 5

During a data mining project, an analyst discovers that a significant number of records have a negative value for the age field. What is the most appropriate first step?

Accepted Answer

Investigate the source system for data entry errors.. The first step in handling anomalous data like negative ages is to investigate the source system for data entry errors. This aligns with the data mining process, where understanding the root cause of data quality issues is critical before applying any imputation or removal techniques. Without investigation, you risk masking systemic problems that could affect all records.

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Impute using regression.

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Replace negative age with the mean age.

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Remove all records with negative age.

Question 6

A financial institution is merging transaction data from two different systems. System A stores currency amounts as integers in cents, and System B stores as decimals in dollars. What is the best way to integrate the data?

Accepted Answer

Convert System A amounts to dollars by dividing by 100.. Converting System A's integer cents to dollars by dividing by 100 ensures both datasets share a consistent unit (dollars) and numeric data type (decimal). This direct transformation eliminates ambiguity in aggregation and reporting, as financial calculations require uniform precision and scale. Using a transformation layer or storing as strings would introduce unnecessary complexity or risk of rounding errors.

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Keep both as is and use a transformation layer.

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Store all amounts as strings to preserve precision.

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Convert System B amounts to cents by multiplying by 100.

Question 7

A data engineer is tasked with acquiring data from a third-party vendor that provides daily file drops via SFTP. The files are large (10 GB each). The pipeline must load data into a data warehouse. Which approach optimizes for speed and reliability?

Accepted Answer

Stream the file directly from SFTP into warehouse using a data pipeline tool. Streaming the file directly from SFTP into the warehouse using a data pipeline tool (e.g., Apache NiFi, Airbyte, or Fivetran) eliminates the intermediate staging step, reducing disk I/O and latency. This approach leverages incremental processing and parallel streams to handle large 10 GB files efficiently, while built-in retry and checkpoint mechanisms ensure reliability against network interruptions.

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Download the file to a staging server, then bulk insert into warehouse

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Have the vendor push data via API instead of SFTP

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Split the file into smaller chunks and load concurrently

Question 8

A healthcare data analyst is presenting findings on patient readmission rates to a group of hospital administrators. The analysis reveals a 15% increase in readmissions over the past quarter for patients aged 65+ from a specific zip code. However, the administrators are skeptical because previous quarterly reports showed no such trend, and they suspect data quality issues. The analyst must communicate this insight effectively while maintaining credibility. Which of the following approaches should the analyst take?

Accepted Answer

Acknowledge the discrepancy and explain possible reasons such as changes in data collection methods or patient population. It demonstrates the core competency of 'Communicating Data Insights' by acknowledging the discrepancy between the current finding and previous reports, which builds trust with skeptical stakeholders. By explaining possible reasons such as changes in data collection methods or patient population, the analyst maintains credibility and invites collaborative investigation into data quality issues, rather than dismissing concerns or hiding details.

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Emphasize the statistical significance of the finding and ignore previous reports

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Present the data without any explanation and let them draw conclusions

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Remove the demographic detail to avoid controversy

Question 9

An IT operations team monitors 200 servers. Each server reports CPU utilization (0-100%) every five minutes for the past year. The team wants to visualize the data to identify servers that are consistently over 80% utilization and detect any unusual spikes. They have a large dataset with 100,000+ records per server. The current visualization is a single scatter plot with CPU utilization on the y-axis, time on the x-axis, and each server as a different colored point. The chart is extremely cluttered, with points overlapping and colors indistinguishable. What should the team do to improve the visualization?

Accepted Answer

Use a heatmap showing CPU utilization over time per server, or create small multiple charts (one per server). With 200 servers and 100,000+ records per server, a single scatter plot is too cluttered. A heatmap can show CPU utilization over time per server using color intensity, or small multiples (one chart per server) can separate the data. Both approaches reduce overlap and make patterns like consistent over-80% utilization and spikes visible.

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Switch to a line chart with each server as a separate line

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Add a trend line to each server's data and remove the individual points

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Increase the size of the data points to make them more visible

Question 10

A data governance team is drafting a policy for handling personally identifiable information (PII). According to data governance best practices, which document should define the classification levels and handling procedures?

Accepted Answer

Data classification policy. The data classification policy is the authoritative document that defines classification levels (e.g., public, internal, confidential, restricted) and specifies handling procedures for each category, including PII. This aligns with data governance best practices, as it establishes the rules for labeling, storing, transmitting, and disposing of sensitive data. A data dictionary describes metadata and schema, not classification rules.

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Data dictionary

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Data quality report

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Data flow diagram

Question 11

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.

Answer

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 12

A data analyst is presenting results from a customer segmentation analysis to a non-technical audience. The segmentation was performed using k-means clustering, and the analyst wants to explain the characteristics of each cluster. Which visualization is most appropriate for this audience?

Accepted Answer

A bar chart comparing the average values of key metrics for each cluster.. A bar chart comparing average values of key metrics per cluster is the most appropriate choice because it directly translates the numerical output of k-means clustering into a simple, intuitive visual that non-technical audiences can easily interpret. Unlike complex multivariate plots, a bar chart focuses on the distinct characteristics of each cluster without requiring knowledge of clustering algorithms or dimensionality reduction. This aligns with the DA0-001 objective of communicating data insights effectively to stakeholders.

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A parallel coordinates plot displaying all variables used in clustering.

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A heatmap of the distance matrix between cluster centroids.

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A scatter plot with two principal components showing cluster boundaries.

Question 13

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 14

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 15

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 16

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 17

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 18

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.

Answer

Increase the request rate

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Use multiple API keys

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Download a precompiled dataset

Question 19

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.

Answer

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.

Answer

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

Question 20

A data analyst is reviewing the error log from a nightly batch load. What is the most likely cause of the error?

Accepted Answer

A row with the same primary key was already loaded in a previous batch.. In nightly batch loads, data is typically inserted into a target table. If a row with a primary key that already exists is attempted to be inserted, the database will raise a primary key constraint violation error. This is the most likely cause of such errors because batch loads often assume unique data but duplicates can occur when previous loads have already inserted those rows.

Answer

The data type of order_id is incorrect.

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The source and target schemas are mismatched.

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The order_id field contains null values.