Pass the CompTIA CompTIA Data+ DA0-002 Questions and answers with CertsForce

This question falls under the Data Analysis domain, 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 falls under the Data Analysis domain, focusing on analytical methods for dashboards. The analyst wants to identify patterns in historical data for further study, which points to a specific type of analytics.

Predictive (Option A): Predictive analytics forecasts future outcomes, not focused on identifying patterns for further study.

Prescriptive (Option B): Prescriptive analytics provides recommendations, which goes beyond identifying patterns.

Diagnostic (Option C): Diagnostic analytics examines historical data to identify patterns, trends, and correlations, enabling further investigation, which fits the scenario.

Descriptive (Option D): Descriptive analytics summarizes what happened but doesn’t focus on identifying patterns for deeper study.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and diagnostic analytics is best for pattern identification in historical data.

This question pertains to the Data Governance domain, focusing on data quality and report performance optimization. The report is slow despite no technical issues, suggesting a data-related inefficiency.

Check that the appropriate filters are applied (Option A): Applying filters reduces the dataset size by excluding irrelevant data, improving report performance. This is a logical next step after ruling out technical issues.

Check data source connections (Option B): The analyst already reviewed the system and found no operational issues, so connectivity is likely not the problem.

Check for data structure changes in the report (Option C): While possible, this is a deeper investigation step and less likely to be the immediate cause of slowness.

Check whether other peers have the same issue (Option D): This might confirm the issue’s scope but doesn’t directly address the performance problem.

The DA0-002 Data Governance domain emphasizes "data quality control concepts," including optimizing report performance through techniques like filtering.

This question falls under the Data Analysis domain, focusing on selecting the appropriate function type to filter data in a query. The task is to flag customers who made a purchase in January, which involves a conditional check.

Statistical (Option A): Statistical functions (e.g., AVG, STDEV) analyze data distributions, not suitable for flagging specific months.

Logical (Option B): Logical functions (e.g., WHERE Month = 'January' in SQL) are used to apply conditions and flag rows based on criteria, which fits the task.

Mathematical (Option C): Mathematical functions (e.g., SUM, ROUND) perform calculations, not conditional flagging.

Date (Option D): Date functions (e.g., MONTH()) manipulate dates, but the Month column is already in text format, so a logical comparison is sufficient.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and logical functions are best for conditional flagging.

This question pertains to the Data Governance domain, focusing on data privacy and security. The task is to share a spreadsheet with sensitive information while protecting personal details.

Using a UUID in the data file (Option A): A UUID (Universally Unique Identifier) can anonymize records, but if other PII (e.g., names) remains, personal details are still exposed.

Redacting all PII (Option B): Redacting personally identifiable information (PII) removes sensitive details (e.g., names, addresses), ensuring personal information isn’t revealed while sharing the spreadsheet.

Adding access controls to the ID column (Option C): Access controls limit who can view the data, but the question focuses on the spreadsheet content itself, not access.

Encrypting the spreadsheet (Option D): Encryption protects the file during transmission, but once opened, personal details are still visible unless redacted.

The DA0-002 Data Governance domain includes "data privacy concepts," and redacting PII is the most direct method to protect personal details in a shared spreadsheet.

This question pertains to the Data Concepts and Environments domain, focusing on data repositories. The task is to identify a repository that stores raw, unformatted data.

Data warehouse (Option A): A data warehouse stores structured, processed data in a predefined schema, not raw data.

Data silo (Option B): A data silo is an isolated repository, often structured, not designed for raw data storage.

Data mart (Option C): A data mart is a subset of a data warehouse, also storing structured data.

Data lake (Option D): A data lake stores raw, unformatted data in its original format (structured, semi-structured, or unstructured), making it the correct choice.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data lake is designed for raw data storage.

This question falls under the Data Concepts and Environments domain, 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 falls under the Data Analysis domain, focusing on methods to gather data for process improvement. The IT manager needs to review user concerns about wait times, which requires collecting feedback.

Infographic (Option A): An infographic visualizes data but isn’t a method for gathering feedback.

KPI (Option B): KPIs (e.g., average resolution time) measure performance but don’t directly gather user feedback.

Survey (Option C): A survey collects detailed feedback from users about their experiences, such as wait times, making it the best method for this review.

UAT (Option D): User Acceptance Testing validates software functionality, not support processes.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and surveys are a standard method for collecting user feedback to analyze and improve processes.

This question pertains to the Data Concepts and Environments domain, focusing on the purpose of data lineage. Data lineage involves tracking the lifecycle of data.

To see the steps and path of data flow through different systems (Option A): This describes a data flow diagram, not data lineage, which focuses on transformations rather than just flow.

To better understand the granularity of data variable relationships (Option B): This relates to data modeling, not the purpose of data lineage.

To track data transformations from acquisition through reporting (Option C): Data lineage tracks the journey of data, including transformations (e.g., cleaning, aggregation) from its source to its final use in reporting, which is its primary purpose.

To look up data definitions, ensuring consistent use across business units (Option D): This describes a data dictionary, not data lineage.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and data lineage specifically tracks transformations across the data lifecycle.