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CompTIA DA0-002 Exam Questions
CompTIA Data+ (2025) (Page 10 )

Updated On: 31-Mar-2026
Page 5 of 26
PDF with 111 Questions

A data analyst receives a notification that a customized report is taking too long to load. After reviewing the system, the analyst does not find technical or operational issues.
Which of the following should the analyst try next?

  1. Check that the appropriate filters are applied.
  2. Check data source connections.
  3. Check for data structure changes in the report.
  4. Check whether other peers have the same issue.

Answer(s): A

Explanation:

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.


Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.



Which of the following best describes the reason an analyst would reference a data dictionary versus a source's metadata?

  1. To gather information and resources about the data
  2. To find the content and specific attributes for a dataset
  3. To find a summary of basic information about the dataset
  4. To gather information about the availability of the data

Answer(s): B

Explanation:

This question is part of the Data Concepts and Environments domain, focusing on the purpose of data documentation tools like data dictionaries and metadata. The question compares their uses.

To gather information and resources about the data (Option A): This is too vague and not specific to a data dictionary's purpose.

To find the content and specific attributes for a dataset (Option B): A data dictionary provides detailed definitions of data elements (e.g., field names, types, descriptions), which is more specific than metadata, which often includes broader information like creation date or source.

To find a summary of basic information about the dataset (Option C): This better describes metadata, which provides high-level summaries, not detailed attributes.

To gather information about the availability of the data (Option D): Neither a data dictionary nor metadata typically focuses on data availability.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data dictionary is specifically used to find detailed attributes of a dataset.


Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.



A data analyst is joining two tables with different content and one common field.
Which of the following should the analyst do to most efficiently meet this requirement?

  1. Match the records of the related columns and merge the tables.
  2. Create a cluster to facilitate data integration between the tables.
  3. Explode both tables to identify unique values and reorder the fields in one table.
  4. Append the values of the matching columns and concatenate the other data fields.

Answer(s): A

Explanation:

This question falls under the Data Acquisition and Preparation domain, focusing on combining data from multiple tables. The tables have different content but share a common field, indicating a join operation.

Match the records of the related columns and merge the tables (Option A): This describes a join operation, where records are matched on the common field (e.g., a key like Customer_ID) and the tables are merged, which is the most efficient method.

Create a cluster to facilitate data integration between the tables (Option B): Clustering is a machine learning technique, not a method for joining tables.

Explode both tables to identify unique values and reorder the fields in one table (Option C):
Exploding is used in nested data (e.g., JSON arrays), and this approach is overly complex and unnecessary.

Append the values of the matching columns and concatenate the other data fields (Option D):
Appending stacks tables vertically, and concatenation applies to text, neither of which is appropriate for joining tables with a common field.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as joining tables using a common field.


Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.



A data analyst pulls a table similar to the following one:

ID Type TypeID Phone

1 Full Time Full Time 1 Mobile

2 Part Time Part Time 2 Work

3 Full Time Full Time 3 Mobile

Which of the following best explains the data issue with TypeID?

  1. Redundancy
  2. Outlier
  3. Missing data
  4. Duplication

Answer(s): A

Explanation:

This question is part of the Data Concepts and Environments domain, 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.


Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.



Which of the following AI types is the best option for time-series forecasting?

  1. Generative AI
  2. Foundational models
  3. Natural language processing
  4. Robotic process automation

Answer(s): B

Explanation:

Foundational models are large AI models trained on vast amounts of data, often exhibiting strong generalization capabilities.
While not specifically architected for time-series, their ability to learn complex patterns could potentially be leveraged for forecasting tasks through fine-tuning or specialized architectures built upon them.

In reality, the best AI types specifically designed for time-series forecasting include:

Recurrent Neural Networks (RNNs), especially LSTMs and GRUs: These architectures are designed to handle sequential data and capture temporal dependencies.

Transformer Networks: Originally developed for NLP, Transformers have shown remarkable success in time-series forecasting due to their ability to capture long-range dependencies.

Traditional statistical models: ARIMA, Exponential Smoothing, and other statistical methods remain powerful and interpretable options for time-series analysis.

Therefore, while "foundational models" have some potential, it's important to understand that they aren't the primary or specifically designed AI type for time-series forecasting.



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