Free Practice Questions for CompTIA DA0-002 Exam

This question pertains to theData Governancedomain, focusing on data classification for security purposes. User information databases typically contain personal data, and security triggers (e.g., alerts for unauthorized access) require classifying data to determine protection levels.

Public (Option A): Public data is openly accessible (e.g., company brochures), not suitable for user information requiring security triggers.

Open (Option B): Open isn’t a standard data classification; it’s similar to public and not applicable here.

Sensitive (Option C): Sensitive data includes information that, if exposed, could cause harm (e.g., user emails, roles), which fits user information and warrants security triggers.

Non-Sensitive (Option D): Non-sensitive data doesn’t require protection, so it wouldn’t need security triggers.

Private (Option E): Private data includes PII (e.g., names, addresses) in user information databases, requiring security measures like triggers to protect against breaches.

Encrypted (Option F): Encrypted refers to a data state, not a classification; data can be classified as private or sensitive and then encrypted.

The DA0-002 Data Governance domain includes "data quality control concepts," such as classifying data to apply appropriate security measures. Sensitive and private classifications are most relevant for user information.

This question falls under theData Concepts and Environmentsdomain, focusing on types of databases. The task is to identify a NoSQL database among the options.

PostgreSQL (Option A): PostgreSQL is a relational (SQL) database, not NoSQL.

MySQL (Option B): MySQL is a relational (SQL) database, not NoSQL.

Oracle (Option C): Oracle Database is a relational (SQL) database, not NoSQL.

MongoDB (Option D): MongoDB is a NoSQL database that uses a document-based model, storing data in JSON-like structures, making it the correct choice.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and MongoDB is a well-known NoSQL database.

This question pertains to theData Governancedomain, focusing on troubleshooting andmaintaining data quality in software processes. The analyst has already tried basic debugging and confirmed compatibility, so the next step involves seeking external resources.

Use the old software and preexisting code, since both were functional (Option A): Reverting to old software avoids solving the issue and may introduce other risks (e.g., security vulnerabilities).

Contact the IT department and inform them that the software has a bug (Option B): Assuming a bug without further investigation is premature, especially since compatibility was confirmed.

Escalate to the department manager and ask for assistance (Option C): Escalation is a later step after exhausting technical troubleshooting options.

Research the issue online and see if a solution is available (Option D): Researching online (e.g., forums, documentation) is a logical next step to find solutions or identify known issues, especially after local debugging fails.

The DA0-002 Data Governance domain includes "data quality control concepts," and researching online is a standard troubleshooting step to maintain data process integrity.

This question falls under theData Concepts and Environmentsdomain, focusing on understanding table types in data warehousing. The task is to identify a table that holds relational keys and numeric values, typically used in a star schema.

Fact (Option A): Fact tables in a star schema store quantitative data (numeric values, e.g., sales amounts) and foreign keys (relational keys) linking to dimension tables, making this the correct choice.

Graph (Option B): Graph tables are used in graph databases for relationships (e.g., nodes, edges), not typically for relational keys and numeric values in a traditional sense.

Dimensional (Option C): Dimension tables store descriptive attributes (e.g., product names) and primary keys, not typically numeric measures.

Transactional (Option D): Transactional tables are used in OLTP systems and may contain numeric values, but they’re not specifically designed for relational keys in a data warehousing context.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and fact tables are designed to hold relational keys and numeric values in a data warehouse.

This question is part of theData Concepts and Environmentsdomain, focusing on identifying data quality issues. The table shows Type and TypeID columns, where TypeID seems to repeat information from Type with an additional identifier.

Redundancy (Option A): The TypeID column (e.g., "Full Time 1") redundantly includes the Type value ("Full Time") with an extra identifier, which is unnecessary and could be simplified by using a numeric ID instead.

Outlier (Option B): Outliers are data points that deviate significantly, which isn’t applicable here.

Missing data (Option C): There are no missing values in the table.

Duplication (Option D): Duplication refers to identical rows, but the rows here are unique; the issue is with the column content.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and redundancy is a common data quality issue in schema design.

This question pertains to theData Concepts and Environmentsdomain, focusing on selecting the appropriate repository for structured data across an entire company. The requirement for a predefined structure narrows the options.

Data mart (Option A): A data mart stores structured data for a specific business area (e.g., sales), not the whole company.

Data warehouse (Option B): A data warehouse is designed to store structured data from across the entire company in a predefined schema, optimized for analytics and reporting.

Data silo (Option C): A data silo is an isolated repository, often structured, but not designed for company-wide integration.

Data lake (Option D): A data lake stores raw data (structured and unstructured) without a predefined structure, not suitable for this requirement.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data warehouse is ideal for company-wide structured data.

This question falls under theData Analysisdomain of CompTIA Data+ DA0-002, which involves using SQL queries to analyze data and address issues like duplicates in datasets. The issue here is that the initial query counts all instances of EMPLOYEE_ID in the JOBS table, but employees can have multiple jobs, leading to an inflated head count. The goal is to count unique employees.

SELECT JOB_TYPE, COUNT DISTINCT(EMPLOYEE_ID) FROM JOBS (Option A): This query is syntactically incorrect because COUNT DISTINCT(EMPLOYEE_ID) should use parentheses as COUNT(DISTINCT EMPLOYEE_ID). It also groups by JOB_TYPE, which is unnecessary for a total head count.

SELECT DISTINCT COUNT(EMPLOYEE_ID) FROM JOBS (Option B): This query is incorrect because DISTINCT applies to the rows returned, not the COUNT function directly. It doesn’t address the duplicate EMPLOYEE_ID issue.

SELECT JOB_TYPE, COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS (Option C): While this query correctly uses COUNT(DISTINCT EMPLOYEE_ID) to count unique employees, grouping by JOB_TYPE breaks the count into separate groups, which isn’t required for a total head count.

SELECT COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS (Option D): This query correctly counts only unique EMPLOYEE_IDs by using the DISTINCT keyword within the COUNT function, providing an accurate total head count without grouping.

The DA0-002 Data Analysis domain emphasizes "given a scenario, applying the appropriate descriptive statistical methods using SQL queries," which includes handling duplicates with functions like COUNT(DISTINCT). Option D is the most direct and accurate method for a total unique head count.

This question falls under theData Analysisdomain, focusing on statistical measures. The mode is the value that appears most frequently in a dataset.

Monday: 455

Tuesday: 346

Wednesday: 382

Thursday: 563

Friday: 887

Saturday: 934

Sunday: 346

The value 346 appears twice (Tuesday and Sunday), while all other values (455, 382, 563, 887, 934) appear once. Thus, the mode is 346.

Option A: 346– Correct, as it’s the most frequent value.

Option B: 446– Incorrect, as 446 doesn’t appear in the dataset.

Option C: 455– Incorrect, as 455 appears only once.

Option D: 559– Incorrect, as 559 doesn’t appear in the dataset.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and the mode is a fundamental measure of central tendency.

This question pertains to theData Governancedomain, focusing on data quality issues that affect analysis. The table contains duplicate rows, which would skew the average score calculation if not addressed.

Student 123: Math (100), Biology (80), Biology (80)– Duplicate Biology score.

Student 234: Math (96), Math (96)– Duplicate Math score.

Student 345: Biology (88)– No duplicates.

Duplication (Option A): The table has duplicate rows (e.g., Student 123’s Biology score of 80 appears twice), which would inflate the average if not removed. The analyst should have checked for duplicates before calculating the average.

Redundancy (Option B): Redundancy refers to unnecessary fields (e.g., storing the same data in multiple columns), not duplicate rows.

Binning (Option C): Binning groups data into categories, not relevant for addressing duplicates in averaging.

Grouping (Option D): Grouping (e.g., GROUP BY in SQL) might be part of the solution,but the issue to identify is duplication.

The DA0-002 Data Governance domain includes "data quality control concepts," and checking for duplication is critical to ensure accurate calculations like averages.