DA0-002 Exam Dumps - CompTIA Data+ Exam (2025)

This question pertains to the Data Concepts and Environments domain, focusing on the content of metadata. Metadata describes data attributes, and the task is to identify what it typically includes.

Transformations (Option A): Transformations (e.g., data cleaning steps) are part of data lineage, not metadata.

Data lineage (Option B): Data lineage tracks data flow and transformations, which is related to metadata but not a direct component.

Syntax (Option C): Syntax refers to code structure, not a metadata component.

Variable types (Option D): Metadata includes information about data fields, such as variable types (e.g., integer, string), which is a standard component.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and metadata typically contains details like variable types to describe the dataset.

This question pertains to the Data Governance domain, focusing on ensuring data quality and reliability in dashboards over time. Daily troubleshooting indicates a recurring issue, and the task is to validate data moving forward.

Inquiring about structure changes (Option A): This might identify past issues but doesn’t provide ongoing validation.

Setting up monitoring alerts (Option B): Monitoring alerts can automatically notify the analyst of data issues (e.g., missing updates, errors), providing a proactive way to validate data continuously.

Reaching out to users daily (Option C): This is inefficient and reactive, not a sustainable validation method.

Rebuilding the dashboard (Option D): Rebuilding might fix current issues but doesn’t ensure future validation.

The DA0-002 Data Governance domain includes "data quality control concepts," such as implementing monitoring to ensure data reliability in dashboards.

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 falls under the Data Analysis domain, focusing on analyzing discrepancies in data reports. The total distinct messages are fewer than the total emails sent, indicating a specific issue.

The aggregation did not include all emails (Option A): If the aggregation missed emails, the total sent would be lower, not the distinct count.

Some emails were not delivered (Option B): Undelivered emails would reduce the total sent, but the scenario implies the total sent is accurate.

The report failed to run properly (Option C): A report failure would likely cause broader issues, not a specific discrepancy between distinct and total counts.

A recipient received duplicate emails (Option D): If recipients received duplicates, the total emails sent would be higher than the distinct messages (unique email content), explaining the difference.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and identifying duplicates is a common analysis task to explain such discrepancies.

This question is related to the Data Governance domain of DA0-002, which includes understanding processes like User Acceptance Testing (UAT) to ensure data-related applications meet governance and quality standards. UAT is a critical step in ensuring software aligns with user needs and organizational requirements.

To begin the software application development process to enhance user experience (Option A): UAT occurs near the end of development, not at the beginning.

To ensure all parts of the software application work together after each sprint (Option B): This describes integration testing, not UAT, which focuses on user validation.

To review software application crashes, create patches, and deploy to users (Option C): This refers to post-deployment maintenance, not UAT.

To validate and verify that a software application meets the needs and requirements of users (Option D): UAT is specifically designed to ensure the software meets user requirements and functions as intended in a real-world scenario, aligning with governance standards for quality.

The DA0-002 Data Governance domain emphasizes "data quality control concepts" (similar to DA0-001, web ID: 1), which include ensuring applications meet user needs through processes like UAT.

This question falls under the Data Governance domain, focusing on troubleshooting data freshness issues in dashboards. The dashboard shows stale data despite a successful refresh, and the server migration updated naming conventions, suggesting a potential mismatch.

Confirm the dashboard is pointed to the newest dataset (Option A): The server migration updated dataset naming conventions, so the dashboard might still be pointing to an old dataset name, causing stale data. Confirming the dataset connection is the first step.

Filter the data in the dashboard (Option B): Filtering might adjust the view but doesn’t address the root cause of stale data.

Escalate user permissions on the server (Option C): Permissions issues would likely prevent access, not cause stale data, especially since the dataset refreshed successfully.

Verify that the dashboard subscription is not expired (Option D): An expired subscription might prevent access, but the dashboard is accessible, just showing stale data.

The DA0-002 Data Governance domain includes "data quality control concepts," such as ensuring dashboards connect to the correct, updated datasets after changes like server migrations.

This question falls under the Data Acquisition and Preparation domain of CompTIA Data+ DA0-002, which involves acquiring and combining data from different sources, such as a database and a web API, using tools like Python. The task requires joining the data, which in Python often involves using pandas DataFrames.

Convert the data from the API and database to a varchar format and convert them to pandas DataFrames that are then merged together (Option A): VARCHAR is a database data type for strings, not a format for data exchange or merging in Python, making this incorrect.

Convert the data from the API and database to a JSON format and convert them to pandas DataFrames that are then merged together (Option B): Web APIs commonly return data in JSON format, and databases can export data as JSON. In Python, JSON data can be easily converted to pandas DataFrames using pandas.read_json() or pandas.DataFrame(), and then merged using pandas.merge() on a common key, making this the best approach.

Convert the data from the API and database to a TXT format and convert them to pandas DataFrames that are then merged together (Option C): TXT is a generic text format that lacks structure, making it less efficient for merging compared to JSON.

Convert the data from the API and database to a string format and convert them to pandas DataFrames that are then merged together (Option D): Converting to a string format is vague and not a standard approach for structured data merging in Python.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as combining data from APIs and databases, and JSON is a standard format for this purpose in Python.

This question pertains to the Data Governance domain, which in DA0-002 includes ensuring data quality and freshness, especially in dashboards. The issue is that the mean response time isn’t updating, while traffic data is current, indicating a potential issue with the data refresh process for the response time metric.

Refactoring the code base (Option A): Refactoring might improve long-term performance but doesn’t directly address verifying data freshness.

Testing for network connectivity issues (Option B): Network issues could cause delays, but since traffic data is updating, connectivity is likely not the issue.

Checking the last time the calculation script ran (Option C): Mean response time is a calculated metric, likely derived from a script. Checking when the script last ran directly verifies if the data refresh process failed, making this the best approach.

Determining the number of calls with no timestamps (Option D): Missing timestamps might indicate data quality issues, but it doesn’t directly verify why the mean response time isn’t updating.

The DA0-002 Data Governance domain focuses on "data quality control concepts," including ensuring data freshness in reporting. Checking the script’s last run time aligns with this objective.