DA0-002 Practice Questions 2 — 50 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.

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Add a 3D effect to bars

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Include a company logo in the chart area

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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.

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Directly write each message to a relational database

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Load directly into a data warehouse

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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.

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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.

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Conduct a survey

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Organize focus groups

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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

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ORDER BY

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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.

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No one can read from the production folder except DataAnalyst.

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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

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The model has high multicollinearity

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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.

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Data lake ingestion

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ETL

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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.

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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

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Randomly select entire clusters of customers

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Select every nth customer from a list

Question 11

In Looker Studio, you have a data source with daily sales and a separate data source with marketing spend. You want to create a chart that shows sales and marketing spend on the same axis, but the two sources are not joined natively. Which feature should you use to combine them?

Accepted Answer

Data blending. Data blending in Looker Studio lets you combine metrics from two different data sources in a single chart by defining a join key (dimension) shared between them. This is the native mechanism for merging unjoined sources without modifying the underlying data. It is designed exactly for scenarios like combining sales and marketing spend on the same axis.

Answer

Filters

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Community connectors

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Calculated fields

Question 12

Which chart type is best for showing the distribution of a continuous variable, such as customer ages?

Accepted Answer

Histogram. A histogram is the correct choice because it groups continuous data (like customer ages) into bins along a continuous x-axis, displaying the frequency distribution through bar heights. This directly shows the shape, spread, and central tendency of the variable, which is the core requirement for visualizing a continuous distribution.

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Bar chart

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Pie chart

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Box plot

Question 13

A data analyst is tasked with collecting data from multiple spreadsheets provided by different departments. Each spreadsheet has different column names and formats. What is the best first step?

Accepted Answer

Develop a data dictionary and standardize column names. Developing a data dictionary and standardizing column names ensures consistency across all data sources before loading, reducing errors and facilitating integration. Immediately loading data can cause inconsistencies. Discarding mismatched data loses potentially valuable information. Using a machine learning model is an unnecessary and complex first step.

Answer

Discard any mismatched data

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Use a machine learning model to clean data

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Immediately load all data into a database

Question 14

A data analyst is tasked with combining customer data from a CRM system and a billing system. The CRM uses a GUID for customer ID, while billing uses an integer. Which approach should the analyst use to ensure a reliable merge?

Accepted Answer

Standardize the customer ID format and use it as the join key.. Standardizing the customer ID format (e.g., converting the billing integer to a GUID or mapping both to a common string key) ensures a consistent join key across heterogeneous systems. This eliminates type mismatch errors and guarantees that each customer record can be matched reliably, as GUIDs are globally unique and integers are typically sequential, so direct comparison would fail without transformation.

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Use the customer name as the join key.

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Merge using a cross-join and then filter manually.

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Perform a fuzzy match on the customer address.

Question 15

A data analyst uses a CTE to find employees who earn more than the average salary in their department. Which SQL clause is used to define the CTE?

Accepted Answer

WITH. The WITH clause is the standard SQL syntax that introduces a Common Table Expression (CTE), allowing the analyst to define a named temporary result set (e.g., WITH dept_avg AS (SELECT dept, AVG(salary) ...)) that can then be referenced in the main query. This is defined in the SQL standard and supported by PostgreSQL, SQL Server, Oracle, and MySQL 8+. The CTE exists only for the duration of the single statement that follows it.

Answer

DECLARE

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DEFINE

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CTE

Question 16

A data analyst at a retail company is profiling a newly acquired customer table. They observe that the 'last_purchase_date' column contains values such as '2023-13-45', '0000-00-00', and '2023-02-30'. Which data quality dimension is primarily violated?

Accepted Answer

Validity. The date values shown are not valid calendar dates, meaning they fail format and range checks. Validity ensures data adheres to defined rules, such as a date being a real date. Completeness, consistency, and uniqueness address different aspects and do not capture the core problem of impossible dates.

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Uniqueness

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Completeness

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Consistency

Question 17

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?

Accepted Answer

Regular patterns that repeat at fixed intervals.. The seasonal component captures regular, repeating patterns at fixed intervals — for example, higher retail sales every December or every weekend. Because the residual shows no pattern (random noise), the decomposition has successfully isolated the systematic periodic structure into the seasonal component. This is the defining characteristic of seasonality in time series decomposition.

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Cyclical variations lasting more than a year.

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Irregular fluctuations that cannot be predicted.

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A long-term increase or decrease in sales.

Question 18

A data analyst needs to compare sales data from the company's internal CRM with public demographic data from a government census. Which data concept best describes this scenario?

Accepted Answer

Internal vs. External data. The scenario involves comparing internal CRM data (generated and owned by the company) with external government census data (publicly sourced from outside the organization). This directly maps to the Internal vs. External data concept, where internal data is collected within the enterprise (e.g., sales transactions, customer records) and external data is acquired from third-party sources (e.g., census bureaus, market research firms). The key distinction is the data's origin and ownership, not its structure, collection method, or measurement type.

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Primary vs. Secondary data

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Structured vs. Unstructured data

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Quantitative vs. Qualitative data

Question 19

An analyst wants to show the trend of monthly sales over the past two years. Which chart type is most appropriate?

Accepted Answer

Line chart. A line chart is best for showing trends over time.

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Pie chart

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Bar chart

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Scatter plot

Question 20

A data analyst at a marketing agency is working with a dataset containing customer demographics, purchase history, and social media engagement metrics. The agency wants to perform sentiment analysis on unstructured social media comments to identify brand perception. The dataset also includes structured fields like age, income, and purchase amounts. The analyst needs to choose a storage and processing platform that can handle both structured and unstructured data efficiently without requiring extensive schema definition upfront. Which platform should the analyst recommend?

Accepted Answer

Data lake. A data lake is the correct choice because it can store both structured data (e.g., age, income, purchase amounts) and unstructured data (e.g., social media comments) in its native format without requiring a predefined schema. This flexibility allows the analyst to ingest raw social media text for sentiment analysis and later apply schema-on-read for structured queries, avoiding the upfront schema definition needed by other platforms.

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Relational database (RDBMS)

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Data warehouse

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NoSQL document database