DA0-002 Quick Quiz — 10 Questions

Question 1

Refer to the exhibit. A data architect is designing a data dictionary for a relational database. Based on the exhibit, which data concept is being illustrated?

Accepted Answer

Data constraints. The exhibit shows a table definition with column attributes such as NOT NULL, UNIQUE, and PRIMARY KEY, which are data constraints that enforce rules on the data values. Data constraints ensure data integrity by restricting what data can be stored in a column, such as preventing null values or duplicate entries. This directly aligns with the concept of data constraints, making option A correct.

Answer

Data aggregation

Answer

Data normalization

Answer

Data cardinality

Question 2

Which TWO of the following are true about correlation and causation? (Select TWO).

Accepted Answer

Correlation does not imply causation. Correlation measures the strength and direction of a linear relationship between two variables, but it does not imply that one variable causes the other. Causation requires controlled experiments with randomization to rule out confounding variables and establish a cause-effect relationship.

Answer

Correlation measures both linear and nonlinear relationships

Answer

Causation can always be inferred from a controlled experiment without randomization

Answer

If two variables are highly correlated, one must cause the other

Question 3

A business analyst is asked to distinguish between KPIs and metrics for a new dashboard. According to CompTIA Data+, which TWO statements correctly differentiate KPIs from metrics? (Choose two.)

Accepted Answer

KPIs are directly tied to strategic business objectives. KPIs are tied to strategic objectives and are often leading or lagging indicators. Metrics are operational measurements that may support KPIs but are not necessarily strategic.

Answer

KPIs and metrics are interchangeable terms

Answer

Metrics are always more important than KPIs

Answer

KPIs are always lagging indicators

Question 4

Which database concept ensures that data in one table corresponds to data in another table, preventing orphan records?

Accepted Answer

Referential integrity. Referential integrity ensures that foreign key values match primary key values in the referenced table.

Answer

Index

Answer

Foreign key

Answer

Primary key

Question 5

A data engineer is designing a system to store raw sensor data from thousands of IoT devices. The data will be used later for various analytics projects, but the schema is not yet defined. Which storage solution is most appropriate?

Accepted Answer

Data lake. A data lake stores raw data in its native format (e.g., S3, ADLS) without requiring a predefined schema, making it suitable for IoT data.

Answer

Data mart

Answer

Data warehouse

Answer

Relational database

Question 6

A data engineer is designing a data warehouse for a retail company. The fact table must record each sale transaction, including product ID, store ID, date, and quantity sold. The product details (name, category, price) are stored in a separate table. This design is an example of which data modeling concept?

Accepted Answer

Star schema. This design is a classic star schema, where a central fact table (sales transactions) contains foreign keys to dimension tables (product, store, date). The fact table stores quantitative measures (quantity sold) and foreign keys, while dimension tables hold descriptive attributes (product name, category, price). This separation optimizes query performance for OLAP workloads by reducing joins and enabling straightforward aggregations.

Answer

Data lake

Answer

Normalization

Answer

Snowflake schema

Question 7

A data analyst includes a map showing customer locations by zip code. The map reveals exact addresses for a few customers due to data granularity. This violates which principle?

Accepted Answer

Anonymization. The map reveals exact addresses for a few customers due to the granularity of zip code data. This directly violates the principle of anonymization, which requires that data be processed in such a way that individuals cannot be identified. By exposing precise locations, the data is no longer anonymized, as it allows re-identification of specific individuals.

Answer

Informed consent

Answer

Data minimization

Answer

Data quality

Question 8

Refer to the exhibit. A data analyst is trying to extract data from a SQL Server database but receives the error. Which configuration change should the analyst recommend to the database administrator?

Accepted Answer

Enable Mixed Mode authentication on the SQL Server. The error indicates that the server is configured for Windows authentication only, but the login attempt used SQL authentication. Enabling Mixed Mode authentication allows both Windows and SQL authentication, resolving the issue.

Answer

Change the server firewall to allow port 1433

Answer

Use a different extraction tool that supports Windows authentication

Answer

Grant the 'dataminer' user SELECT permissions

Question 9

A business analyst needs to show the monthly sales trend for the past three years. Which chart type is most appropriate?

Accepted Answer

Line chart. Line charts are ideal for displaying trends over time, making them suitable for showing monthly sales trends.

Answer

Histogram

Answer

Pie chart

Answer

Scatter plot

Question 10

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

Answer

Use multiple API keys

Answer

Download a precompiled dataset