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CompTIA DA0-002 Exam Syllabus Topics:

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

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CompTIA Data+ Exam Sample Questions (Q167-Q172):

NEW QUESTION # 167
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: C

Explanation:
Predictive analysis uses historical data and statistical models to forecast future outcomes. Since the goal is to anticipate the number of calls that will be received each day, predictive analysis is the most appropriate method.


NEW QUESTION # 168
A data analyst is joining two tables with different content and one common field. Which of the following should the analyst do to most efficiently meet this requirement?

Answer: C

Explanation:
When two tables contain different data but share a common field, the most efficient approach is to match the records using the common field and merge the tables through a join operation. This combines related information from both tables while preserving the relationships between the records.


NEW QUESTION # 169
A data analyst team needs to segment customers based on customer spending behavior. Given one million rows of data like the information in the following sales order table:
Customer_ID
Region
Amount_spent
Product_category
Quantity_of_items
00123
East
20000
Baby
4
00124
West
30000
Home
6
00125
South
40000
Garden
7
00126
North
50000
Furniture
8
00127
East
60000
Baby
10
Which of the following techniques should the team use for this task?

Answer: C

Explanation:
This question falls under theData Analysisdomain, focusing on techniques for segmenting data. The task is to segment customers based on spending behavior, which involves grouping numerical data (Amount_spent) into categories.
* Standardization (Option A): Standardization scales numerical data to a common range (e.g., z-scores), but it doesn't segment customers into groups.
* Concatenate (Option B): Concatenation combines text fields, not numerical data for segmentation.
* Binning (Option C): Binning involves grouping numerical data into discrete intervals (e.g., low, medium, high spending), which is ideal for segmenting customers based on spending behavior.
* Appending (Option D): Appending combines datasets vertically, not relevant for segmentation.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and binning is a common method for segmenting numerical data like spending amounts.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.


NEW QUESTION # 170
A data analyst wants to analyze sales data for possible customer patterns. Which of the following should the analyst use to complete this task?

Answer: C

Explanation:
Clustering is a data analysis technique that groups customers or transactions with similar characteristics. By identifying these natural groupings within sales data, the analyst can discover customer patterns, behaviors, and trends that may not be immediately apparent.


NEW QUESTION # 171
Which of the following file types separates data using a delimiter?

Answer: A

Explanation:
A CSV file stores data in plain text format where values are separated by a delimiter, typically a comma, making it a standard delimited file type.


NEW QUESTION # 172
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