DA0-002 Practice Test 6 — 10 Questions

Question 1

A dashboard designer wants to maximize the data-ink ratio. Which action should they take?

Accepted Answer

Remove redundant gridlines. The data-ink ratio, a concept from Edward Tufte, is maximized by removing non-data ink — elements that do not convey information. Redundant gridlines add visual clutter without adding data value, so removing them directly increases the ratio. The other options all add non-data ink (3D effects, logos, colorful backgrounds) that reduce the ratio.

Answer

Add a 3D effect to bars

Answer

Include a company logo in the chart area

Answer

Use a colorful background

Question 2

A data engineer is designing a data pipeline to ingest streaming data from IoT sensors. The sensors send data every second, and the pipeline must handle bursts of up to 10,000 messages per second. Which approach is most appropriate for capturing this data before processing?

Accepted Answer

Use a message queue to buffer the incoming data. A message queue (e.g., Apache Kafka, Amazon Kinesis, or RabbitMQ) provides an asynchronous buffer that decouples the high-velocity ingestion (up to 10,000 messages/second) from downstream processing. This allows the pipeline to absorb burst traffic without overwhelming the processing layer, ensures data durability, and supports replayability in case of failures.

Answer

Directly write each message to a relational database

Answer

Load directly into a data warehouse

Answer

Store data in flat files and process in nightly batches

Question 3

A data analyst receives a dataset with a column 'salary' that contains values like '45,000', '55,000', and '65,000'. The analyst notices that the values are stored as text. Which data concept should be applied to convert the salary column from text to numeric format for analysis?

Accepted Answer

Data type conversion. Data type conversion is the correct concept because the salary values are stored as text (string) but need to be converted to a numeric type (e.g., integer or float) for mathematical operations like aggregation or averaging. In tools like Python (pandas `astype(float)`), SQL (`CAST(salary AS INTEGER)`), or Excel (`VALUE()` function), this explicit conversion ensures the data is treated as numbers, not strings. Without conversion, operations like `SUM` or `AVG` would fail or produce incorrect results.

Answer

Data imputation

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Data validation

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Data normalization

Question 4

A data analyst needs to collect customer sentiment data from social media platforms. Which data acquisition method is most appropriate?

Accepted Answer

Use web scraping. Web scraping is the most appropriate method because it allows the data analyst to programmatically extract unstructured customer sentiment data (e.g., posts, comments, reviews) directly from social media platforms using HTTP requests and HTML parsing. Unlike surveys or focus groups, scraping can collect large volumes of real-time, publicly available data without relying on self-reported or curated responses.

Answer

Conduct a survey

Answer

Organize focus groups

Answer

Query the internal CRM

Question 5

A data analyst is extracting data from a relational database using SQL. Which clause is essential for limiting the rows retrieved to only those needed?

Accepted Answer

WHERE. WHERE. The WHERE clause is used to filter rows from a table based on specified conditions, limiting the result set to only the rows that meet those conditions. Option A (GROUP BY) is incorrect because it groups rows with same values into summary rows, not for filtering. Option B (ORDER BY) is incorrect because it sorts the result set, not filters. Option D (HAVING) is incorrect because it filters groups after aggregation, not individual rows. Therefore, WHERE is essential for limiting rows retrieved.

Answer

GROUP BY

Answer

ORDER BY

Answer

HAVING

Question 6

Refer to the exhibit. A data analyst is trying to understand access permissions for the company data folder. Which statement accurately describes the effective permissions?

Accepted Answer

DataAnalyst can read objects in the production folder except those in the sensitive subfolder.. The exhibit shows an access control policy that grants the DataAnalyst user read permission on the production folder, but includes an explicit deny rule for the sensitive subfolder specified via a path condition. In most access control systems, explicit deny rules take precedence over allow rules, so the deny on the sensitive subfolder overrides the allow on the production folder, effectively blocking read access to objects in the sensitive subfolder while permitting reads elsewhere in the production folder.

Answer

DataAnalyst can read all objects in the production folder, including the sensitive subfolder.

Answer

No one can read from the production folder except DataAnalyst.

Answer

Only DataAnalyst is allowed to read from the entire production folder.

Question 7

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?

Accepted Answer

One or more predictors may not be contributing meaningfully. R-squared (0.85) measures how much variance the model explains, while adjusted R-squared (0.80) penalizes for the number of predictors and only increases if a new predictor improves the model more than chance would predict. A notable drop between the two (0.05) suggests that at least one predictor is not contributing meaningfully and may be adding noise rather than explanatory power.

Answer

The model is overfitted, so all predictors should be removed

Answer

The model has high multicollinearity

Answer

The residuals are not normally distributed

Question 8

A company is ingesting data from multiple sources into a cloud data warehouse. They decide to load the data raw and then perform transformations within the warehouse. Which approach does this describe?

Accepted Answer

ELT. ELT (Extract, Load, Transform) loads raw data first, then transforms it inside the data warehouse, as opposed to ETL which transforms before loading.

Answer

Data lake ingestion

Answer

ETL

Answer

Stream processing

Question 9

A financial institution wants to analyze transaction networks to detect fraud rings. Which database type is best suited for this analysis?

Accepted Answer

Graph database. A graph database is designed to store and traverse relationships between entities, making it ideal for analyzing transaction networks where connections between accounts, merchants, and transactions reveal fraud rings. Its native graph model (nodes and edges) allows efficient pattern matching and pathfinding queries, such as detecting circular transactions or shared attributes, which are common in fraud detection.

Answer

Wide-column store

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Key-value store

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Document store

Question 10

A data analyst needs to perform a stratified random sample of a customer database. Which TWO steps are essential for this sampling method? (Select two.)

Accepted Answer

Randomly select a proportional number from each stratum. Option D is correct because stratified random sampling begins by partitioning the population into homogeneous subgroups called strata, typically based on a shared characteristic such as age, region, or customer tier, so that each subgroup is internally similar. Option C is correct because, after the strata are formed, the analyst must draw a random sample from each stratum, usually in a number proportional to that stratum's size in the population, ensuring the sample reflects the population's structure. Option A is incorrect because simple random sampling on the whole population ignores the strata and is a different sampling method. Option B is incorrect because randomly selecting entire clusters describes cluster sampling, not stratified sampling. Option E is incorrect because selecting every nth customer is systematic sampling, which does not require dividing the population into strata.

Answer

Use simple random sampling on the whole population

Answer

Randomly select entire clusters of customers

Answer

Select every nth customer from a list