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CompTIA DA0-002 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Mining25%- Data enrichment and integration
  • 1. Data mapping and lineage
  • 2. Derived columns and calculated fields
  • 3. Joining and merging datasets
- Data acquisition and collection
  • 1. APIs and connectors
  • 2. ETL/ELT processes
  • 3. Data sources and ingestion methods
- Data preparation and cleansing
  • 1. Data cleaning techniques
  • 2. Handling missing values and duplicates
  • 3. Data normalization and transformation
Topic 2: Data Concepts and Environments20%- Data structures and formats
  • 1. Structured, semi-structured, and unstructured data
  • 2. Common data formats (JSON, XML, CSV)
  • 3. File types and their use cases
- Data types and metadata
  • 1. Data types (string, numeric, Boolean, date)
  • 2. Metadata and its role in data management
- Database fundamentals
  • 1. Relational vs. non-relational databases
  • 2. Schemas and data models
  • 3. Cloud and containerized data environments
Topic 3: Data Governance, Quality, and Controls16%- Security and privacy
  • 1. Foundational AI considerations in data lifecycle
  • 2. Audit trails and logging
  • 3. Access controls and encryption
  • 4. Data classification and protection
- Data quality management
  • 1. Remediation strategies
  • 2. Quality assessment and monitoring
  • 3. Quality dimensions (accuracy, completeness, consistency)
- Data governance frameworks
  • 1. Data stewardship and ownership
  • 2. Policies, standards, and procedures
  • 3. Compliance requirements (GDPR, CCPA, etc.)
Topic 4: Visualization15%- Storytelling with data
  • 1. Narrative techniques for data communication
  • 2. Executive summaries and key insights
  • 3. Audience-appropriate visuals
- Dashboard and report design
  • 1. Dashboard components and layout
  • 2. Chart types and their applications
  • 3. Visualization best practices
Topic 5: Data Analysis24%- Descriptive statistics
  • 1. Measures of dispersion
  • 2. Measures of central tendency
  • 3. Data distributions
- Analysis tools and techniques
  • 1. BI platforms and notebooks
  • 2. SQL for data analysis
  • 3. Libraries and functions for analysis
- Inferential statistics
  • 1. Hypothesis testing
  • 2. Correlation and regression basics
  • 3. Confidence intervals

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CompTIA Data+ Exam Sample Questions (Q116-Q121):

NEW QUESTION # 116
A data scientist is presenting a summary speech on current AI trends in the data industry to local high school students. Which of the following should the data scientist consider first when developing the presentation?

Answer: D

Explanation:
The technical knowledge of the audience should be considered first because it determines the appropriate level of detail, terminology, and complexity of the presentation. Since the audience consists of high school students, the content should be tailored to their likely level of understanding to ensure effective communication.


NEW QUESTION # 117
A data analyst must combine service calls into low-, medium-, and high-priority levels in order to analyze organizational responses. Which of the following techniques should the analyst use for this task?

Answer: A

Explanation:
This question pertains to theData Analysisdomain, focusing on techniques for categorizing data. The task involves grouping service calls into priority levels (low, medium, high), which requires segmenting numerical or ordinal data into discrete categories.
* Augmentation (Option A): Augmentation involves adding data (e.g., in machine learning), not categorizing existing data.
* Imputation (Option B): Imputation fills in missing values, not relevant for categorizing priority levels.
* Scaling (Option C): Scaling adjusts numerical data to a common range (e.g., normalization), not suitable for creating priority categories.
* Binning (Option D): Binning groups continuous or ordinal data into discrete categories (e.g., assigning calls to low, medium, or high priority based on a metric like response time), which fits the task.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and binning is a standard technique for categorizing data for analysis.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.


NEW QUESTION # 118
A data analyst needs to get an accurate idea of how data components are automated. Which of the following types of documentation should the analyst review first?

Answer: C

Explanation:
This question pertains to the Data Concepts and Environments domain, focusing on documentation for understanding data processes. The analyst needs to understand automation of data components, which involves data movement and processes.
* Data flow diagram (Option A): A data flow diagram (DFD) visualizes how data moves through systems, including automated processes, making it the best starting point.
* Data explainability report (Option B): This is related to AI/ML model transparency, not data automation.
* Data dictionary (Option C): A data dictionary defines data elements, not how they're automated.
* Data lineage (Option D): Data lineage tracks data origin and transformations but doesn't focus on automation processes.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data flow diagram is key for visualizing automation.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.


NEW QUESTION # 119
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?

Answer: A

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 # 120
A data analyst is creating a new dataset that involves bringing together the following datasets:

Which of the following would be the output if the data analyst does a FULL JOIN?

Answer: C

Explanation:
A FULL JOIN returns all records from both tables. Matching records are combined based on the common ID field, while nonmatching records from either table are still included with null or blank values for the missing columns.
In the source data:
ID 23525 exists in both tables, so Frank is combined with the Chicago address.
ID 11290 exists in both tables, so Martha is combined with the Miami address.
ID 12141 exists only in the first table, so Ellen appears with blank address information.
ID 11040 exists only in the second table, so the address record appears with blank name and date-of-birth information.


NEW QUESTION # 121
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