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[Hardware] Pass Guaranteed Quiz CompTIA - The Best DA0-002 - Dump CompTIA Data+ Exam (2025)

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【Hardware】 Pass Guaranteed Quiz CompTIA - The Best DA0-002 - Dump CompTIA Data+ Exam (2025)

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CompTIA Data+ Exam (2025) Sample Questions (Q93-Q98):NEW QUESTION # 93
A data analyst created a dashboard to illustrate the traffic volume and mean response time for a call center.
The traffic data is current, but the mean response time has not updated for more than an hour. Which of the following is the best way to verify the data's freshness?
  • A. Checking the last time the calculation script ran
  • B. Determining the number of calls with no timestamps
  • C. Testing for network connectivity issues
  • D. Refactoring the code base
Answer: A
Explanation:
This question pertains to theData Governancedomain, 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 forthe 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.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.

NEW QUESTION # 94
An analyst needs to create a collection of dashboards for multiple teams within their organization. Which of the following should the analyst do first before starting the project?
  • A. Determine the delivery method of the dashboards.
  • B. Evaluate the user persona type for the dashboards.
  • C. Evaluate the KPIs for the dashboards.
  • D. Determine the number of team members who need to access the dashboards.
Answer: B
Explanation:
This question pertains to theVisualization and Reportingdomain, focusing on the initial steps in dashboard creation. The analyst is starting a project for multiple teams, so understanding the audience is critical.
* Evaluate the user persona type for the dashboards (Option A): User personas define the needs, roles, and preferences of the audience (e.g., executives vs. analysts), which is the firststep to ensure the dashboards meet user requirements.
* Determine the number of team members who need to access the dashboards (Option B): This is important for access control but not the first step in design.
* Determine the delivery method of the dashboards (Option C): Delivery method (e.g., self-service) is decided later, after understanding user needs.
* Evaluate the KPIs for the dashboards (Option D): KPIs are defined after understanding the user personas and their goals.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and evaluating user personas is the first step in this process.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.

NEW QUESTION # 95
A data analyst wants to find the mean score that students received in science classes. Given the following tables:

Which of the following will produce the desired results?
  • A. COUNT and GROUP
  • B. CONCAT and WHERE
  • C. average and where
  • D. SUM and GROUP
Answer: A

NEW QUESTION # 96
A data analyst learns that a report detailing employee sales is reflecting sales only for the current month.
Which of the following is the most likely cause?
  • A. Report refresh failure
  • B. An error in SQL code
  • C. Connectivity issues
  • D. Lack of permissions
Answer: B
Explanation:
This question falls under theData Analysisdomain, focusing on troubleshooting issues in data reports. The report should show all employee sales but is limited to the current month, suggesting a data retrieval issue.
* Lack of permissions (Option A): Permissions issues would likely prevent access entirely, not limit data to the current month.
* An error in SQL code (Option B): The report likely uses an SQL query to retrieve data, and an error (e.g., a WHERE clause filtering for the current month) could restrict the data to the current month, making this the most likely cause.
* Report refresh failure (Option C): A refresh failure would result in outdated data, not specifically current-month data.
* Connectivity issues (Option D): Connectivity issues would likely prevent the report fromrunning, not limit it to a specific time frame.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and errors in SQL code are a common cause of incorrect data retrieval in reports.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.

NEW QUESTION # 97
Which of the following AI types is the best option for time-series forecasting?
  • A. Natural language processing
  • B. Robotic process automation
  • C. Generative AI
  • D. Foundational models
Answer: D
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.

NEW QUESTION # 98
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