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CompTIA DA0-002 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Visualization: This section of the exam measures skills of a Data Visualisation Specialist and focuses on turning raw data into clear, visual insights. It teaches how to match visual formats like bar charts, heat maps, and line graphs to specific audiences and needs. Candidates must understand how to create dashboards and reports using proper design elements such as labels, layout, branding, and colour schemes. This section also includes best practices for dashboard development and delivery through various platforms and user access levels.
Topic 2
  • Data Mining: This section of the exam measures skills of a Business Intelligence Analyst and covers how data is collected, cleaned, and prepared for analysis. It explains methods like ETL and ELT for data integration, as well as web scraping, API use, and survey data collection. Candidates are expected to identify issues like missing or duplicated data and apply techniques like filtering, sorting, merging, and normalizing. The section also touches on query optimization strategies to improve data handling efficiency.
Topic 3
  • Data Concepts and Environments: This section of the exam measures the skills of a Junior Data Analyst and focuses on understanding core data concepts such as database types, schema structures, and data formats. It highlights differences between structured and unstructured data, compares file types like CSV, JSON, and XML, and introduces key ideas about data dimensions and slowly changing dimensions. Knowing how data is stored and organized helps professionals better prepare for analysis and reporting tasks.
Topic 4
  • This section of the exam measures skills of a Data Governance Associate and introduces principles for keeping data secure, accurate, and compliant. It covers access controls, encryption, classification of sensitive data like PII and PHI, and legal requirements tied to data use. Candidates must know how to apply quality checks, validate data, and manage master data effectively. It also addresses best practices for maintaining integrity through data dictionaries, audits, and standardisation processes.

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CompTIA Data+ Exam Sample Questions (Q129-Q134):

NEW QUESTION # 129
A recent server migration applied an update to dataset naming conventions. Multiple users are now reporting stale information in an existing dashboard. The date in the dataset confirms a successful data refresh. Which of the following should a data analyst do first?

Answer: A

Explanation:
This question falls under theData Governancedomain, 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.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.


NEW QUESTION # 130
Which of the following AI types is the best option for time-series forecasting?

Answer: A

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 # 131
A data analyst is creating a high-level report for the Chief Executive Officer. Which of the following data would most likely be included in the report?

Answer: D

Explanation:
Year-to-date sales numbers provide high-level performance insights appropriate for executive decision-making.


NEW QUESTION # 132
A company needs to display sales numbers on the intranet. This solution needs to be updated every day before the workday starts. Which of the following is the best solution to meet this objective?

Answer: D

Explanation:
A dynamic dashboard that refreshes nightly automatically updates the displayed sales data before employees begin work each day. This provides current information on the intranet without requiring manual report generation or distribution, making it the most efficient and reliable solution.


NEW QUESTION # 133
A data analyst needs to create a report that anticipates the number of calls received daily. Which of the following is the best statistical method to use?

Answer: A

Explanation:
This question falls under theData Analysisdomain, focusing on statistical methods for forecasting. The task is to anticipate (predict) the number of daily calls, which involves looking into the future.
* Predictive (Option A): Predictive analytics uses historical data to forecast future outcomes (e.g., number of calls), which matches the requirement.
* Diagnostic (Option B): Diagnostic analytics identifies causes and patterns in historical data, not future predictions.
* Inferential (Option C): Inferential statistics make generalizations about a population, not specific forecasts.
* Descriptive (Option D): Descriptive analytics summarizes past data, not suitable for anticipating future values.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and predictive analytics is the best method for forecasting future call volumes.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.


NEW QUESTION # 134
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