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CompTIA DA0-002 Exam Syllabus Topics:

SectionWeightObjectives
Data Mining25%- Data preparation and cleansing
  • 1. Data normalization and transformation
  • 2. Handling missing values and duplicates
  • 3. Data cleaning techniques
- Data enrichment and integration
  • 1. Joining and merging datasets
  • 2. Derived columns and calculated fields
  • 3. Data mapping and lineage
- Data acquisition and collection
  • 1. Data sources and ingestion methods
  • 2. APIs and connectors
  • 3. ETL/ELT processes
Visualization15%- Storytelling with data
  • 1. Executive summaries and key insights
  • 2. Narrative techniques for data communication
  • 3. Audience-appropriate visuals
- Dashboard and report design
  • 1. Visualization best practices
  • 2. Chart types and their applications
  • 3. Dashboard components and layout
Data Analysis24%- Analysis tools and techniques
  • 1. Libraries and functions for analysis
  • 2. BI platforms and notebooks
  • 3. SQL for data analysis
- Descriptive statistics
  • 1. Measures of dispersion
  • 2. Data distributions
  • 3. Measures of central tendency
- Inferential statistics
  • 1. Hypothesis testing
  • 2. Correlation and regression basics
  • 3. Confidence intervals
Data Governance, Quality, and Controls16%- Data quality management
  • 1. Quality dimensions (accuracy, completeness, consistency)
  • 2. Remediation strategies
  • 3. Quality assessment and monitoring
- Data governance frameworks
  • 1. Policies, standards, and procedures
  • 2. Data stewardship and ownership
  • 3. Compliance requirements (GDPR, CCPA, etc.)
- Security and privacy
  • 1. Audit trails and logging
  • 2. Access controls and encryption
  • 3. Data classification and protection
  • 4. Foundational AI considerations in data lifecycle
Data Concepts and Environments20%- Data types and metadata
  • 1. Metadata and its role in data management
  • 2. Data types (string, numeric, Boolean, date)
- Database fundamentals
  • 1. Schemas and data models
  • 2. Cloud and containerized data environments
  • 3. Relational vs. non-relational databases
- Data structures and formats
  • 1. Common data formats (JSON, XML, CSV)
  • 2. Structured, semi-structured, and unstructured data
  • 3. File types and their use cases

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CompTIA Data+ Exam Sample Questions (Q46-Q51):

NEW QUESTION # 46
An analyst needs to combine the following two tables. The analyst needs to keep all the records in the Medals table and only the records that match from the Country table.

Which of the following methods should the analyst use for this task?

Answer: B

Explanation:
A join is used to combine rows from two tables based on a common field, in this case Person_ID.
Since all records from the Medals table must be retained and only matching records from the Country table included, the appropriate operation is a left join, which is a type of join.


NEW QUESTION # 47
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: D

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 # 48
An analyst is developing a form to collect data from clients. Which of the following techniques should the analyst use to best ensure data integrity?

Answer: B

Explanation:
Adding rules for data validation ensures inputs meet expected formats and constraints, preserving data integrity during collection.


NEW QUESTION # 49
A data analyst receives a new data source that contains employee IDs, job titles, dates of birth, addresses, years of service, and employees' birth months. Which of the following inconsistencies should the analyst identify?

Answer: B


NEW QUESTION # 50
A data analyst receives a report from a dashboard user stating that their report is taking longer than usual to load. The user needs five components for the dashboard. However, upon investigating, the analyst notices ten components that are attempting to load. Which of the following steps should the analyst take to correct this issue?

Answer: B


NEW QUESTION # 51
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