DA0-002 Practice Test 28 — 15 Questions

Question 1

A data team is building a predictive model. They have data on 'Number of employees' (whole numbers) and 'Revenue' (currency). Which statement correctly compares these data types?

Accepted Answer

Number of employees is discrete; revenue is continuous. 'Number of employees' is a count of distinct entities, making it discrete data (only whole numbers), while 'Revenue' can take any value within a range (including decimals), making it continuous data. Discrete data arises from counting, whereas continuous data arises from measurement.

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Both are continuous data

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Both are ratio data

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Number of employees is qualitative; revenue is quantitative

Question 2

A company uses a data warehouse for reporting. They need to extract data from multiple sources, load it into a staging area, and then transform it before moving to the warehouse. This process is known as:

Accepted Answer

ETL. The process described—extracting data from multiple sources, loading it into a staging area, and then transforming it before moving to the warehouse—is the classic definition of ETL (Extract, Transform, Load). In ETL, transformation occurs after extraction but before loading into the target system, which is exactly what the staging area is used for. This contrasts with ELT, where transformation happens after loading into the warehouse.

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ELT

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Data replication

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Data ingestion

Question 3

A data analyst needs to combine two datasets that have the same columns but different rows. Which operation should they use?

Accepted Answer

Append. (Append) is correct because appending is the standard operation for combining two datasets with identical columns but different rows, stacking the rows from one dataset onto the other. In tools like SQL, this is achieved with the UNION or UNION ALL operator, and in Python pandas, it is done via the `append()` method or `pd.concat()` with axis=0. This operation preserves the column structure while extending the row count.

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Concatenate

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Merge

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Aggregate

Question 4

Which THREE are challenges in acquiring data from external sources? (Select three.)

Accepted Answer

Licensing restrictions. Data format inconsistency occurs when integrating data from different sources. Rate limiting is a common API restriction that limits how much data can be accessed. Licensing restrictions may limit the use or redistribution of acquired data. Data redundancy is an internal data quality issue, not a challenge specific to acquisition. Unauthorized access is a security concern but not a typical acquisition challenge.

Answer

Data redundancy

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Unauthorized access

Question 5

A data analyst needs to create a dashboard that updates automatically every hour. The data source is a large database. Which approach minimizes performance impact?

Accepted Answer

Use incremental refresh only for new or changed data. Incremental refresh queries only new or changed records since the last refresh, drastically reducing data transfer and processing load on the large database. This approach uses change-tracking mechanisms (e.g., timestamps, CDC) to avoid full-table scans, minimizing performance impact while maintaining near-real-time updates.

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Query the entire database each time

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Export the data to Excel and import

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Create a static report monthly

Question 6

A data analyst is validating a dataset acquired from an external source. Which TWO actions are appropriate for data quality assessment?

Accepted Answer

Check for missing values in critical fields. Checking for missing values in critical fields is a fundamental data quality assessment step because missing data can indicate incomplete records, data corruption, or extraction errors. Identifying these gaps early allows the analyst to decide on appropriate handling strategies, such as imputation or rejection, before further processing. This aligns with data profiling best practices in the mining and acquisition phase.

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Delete any rows with null values without review

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Immediately load all data into production

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Transform data to match target system without verification

Question 7

A data analyst needs to communicate a forecast with uncertainty. Which visualization is best?

Accepted Answer

Line chart with confidence intervals. A line chart with confidence intervals is the best choice because it explicitly visualizes the forecast trend over time while also displaying the range of uncertainty (e.g., 95% confidence bands). This allows the data analyst to communicate both the central projection and the variability around it, which is essential for informed decision-making.

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Stacked bar chart

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Pie chart

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Histogram

Question 8

A marketing company is building a customer segmentation model. The data team has access to two sources: a CRM database with customer demographics and purchase history, and a third-party data provider that offers social media activity scores. The CRM data is updated daily, while the third-party data is refreshed weekly on Sundays. The analyst needs to create a unified dataset for the model training scheduled for Wednesday morning. The analyst runs a SQL query to join the two tables on CustomerID, but the resulting dataset has far fewer rows than expected. Upon investigation, the analyst finds that many customers in the CRM do not have matching records in the third-party data. Additionally, some customers in the third-party data have multiple entries due to unresolved duplicates. The analyst must produce the most complete dataset possible while maintaining data quality. Which course of action should the analyst take?

Accepted Answer

First deduplicate the third-party data by keeping the most recent record per CustomerID, then perform a LEFT JOIN from CRM to the deduplicated third-party data.. It first resolves the duplicate issue in the third-party data by keeping the most recent record per CustomerID, ensuring each customer has a single, current social media score. Then, a LEFT JOIN from CRM to the deduplicated third-party data preserves all CRM customers, maximizing completeness while maintaining data quality. This approach aligns with the goal of producing the most complete dataset for model training, as the CRM is the primary source with daily updates.

Answer

Perform an INNER JOIN on CustomerID and then remove duplicates from the result.

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Use only the third-party data because it provides the social media scores needed for segmentation.

Answer

Perform a LEFT JOIN from the third-party data to CRM, then aggregate duplicates by averaging scores.

Question 9

A data analyst wants to show the distribution of customer ages for a retail store. The ages are continuous and the analyst needs to visualize the frequency of different age ranges. Which chart type is most appropriate?

Accepted Answer

Histogram. A histogram is used to display the distribution of a continuous variable by grouping data into bins.

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Bar chart

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Line chart

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Pie chart

Question 10

A data analyst is building a report in Power BI and needs to create a measure that calculates the total sales for the current year, but only for products that have at least one sale in the previous year. Which DAX function combination is most appropriate?

Accepted Answer

CALCULATE with FILTER and SAMEPERIODLASTYEAR. Using CALCULATE with a FILTER that checks for sales in the previous year via SAMEPERIODLASTYEAR or a similar time intelligence filter achieves this dynamic filtering.

Answer

RELATED to pull product data

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TOTALYTD with a filter on Date table

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SUMX with a FILTER on Sales table

Question 11

Refer to the exhibit. A data analyst notices that direct S3 access to files outside the "incoming/" prefix is blocked. Which data governance principle does this policy enforce?

Accepted Answer

Data access control. The policy blocks direct S3 access to files outside the 'incoming/' prefix, which restricts which users or roles can read or write objects in specific S3 prefixes. This is a classic implementation of data access control, as it enforces permissions based on the resource path, ensuring only authorized operations are allowed on designated data. In AWS S3, such restrictions are typically applied via bucket policies or IAM policies that use conditions like `s3:prefix` to limit access.

Answer

Data colocation

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Data retention

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Data encryption

Question 12

A data analyst is performing data acquisition from multiple source files. Which TWO data profiling tasks should the analyst complete before loading the data into the target system?

Accepted Answer

Verify data types and formats. Identifying missing values and verifying data types are fundamental data profiling tasks that help ensure data quality before acquisition. Building models, creating dashboards, or clustering are not part of profiling.

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Create a dashboard for stakeholders

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Build a linear regression model

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Perform cluster analysis

Question 13

A company stores customer data in a relational database with tables for orders, products, and customers. Which type of data best describes this?

Accepted Answer

Structured data. A is correct because the data is stored in a relational database with predefined schemas (tables for orders, products, and customers), which enforces a fixed structure of rows and columns. This makes it structured data, as each field has a specific data type and relationships are defined via foreign keys, enabling efficient querying with SQL.

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Unstructured data

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Qualitative data

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Semi-structured data

Question 14

Match each database concept to its definition.

Accepted Answer

Primary Key: Uniquely identifies each record in a table.. Primary keys uniquely identify records, foreign keys link tables, indexes speed retrieval, and normalization reduces redundancy. Common confusions include swapping primary and foreign key definitions.

Answer

Primary Key: A field that links to the primary key of another table.

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Foreign Key: Uniquely identifies each record in a table.

Question 15

A data analyst is creating a dashboard to monitor server CPU utilization over the past 24 hours. Which chart type is most appropriate for showing the trend of CPU usage over time?

Accepted Answer

Line chart. A line chart is the most appropriate choice for displaying CPU utilization trends over a continuous 24-hour period because it connects data points in chronological order, making it easy to observe peaks, valleys, and overall patterns. The x-axis represents time (hours), and the y-axis represents CPU usage percentage, allowing the analyst to quickly identify when utilization spikes or drops. This aligns with the DA0-001 objective of selecting the correct visualization for time-series data.

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Scatter plot

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Pie chart

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Bar chart