CompTIA DA0-001日本語 : CompTIA Data+ Certification Exam (DA0-001日本語版)

  • Exam Code: DA0-001日本語
  • Exam Name: CompTIA Data+ Certification Exam (DA0-001日本語版)
  • Updated: Sep 01, 2025   Q&As: 365 Questions and Answers

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What are the steps to follow for the registration for the CompTIA DA0-001 Exam

  • Go to the official website of CompTIA Data+ and click on the “Register” button.

  • Fill in all the required fields in the registration form and click on “Submit”.

  • A confirmation email will be sent to you, which contains a link to activate your account and a link to print your exam voucher (you can also opt for an alternative method of delivery).

  • Go through your exam voucher and note down all the details mentioned on it, including: Exam ID, Exam Number, Date, Time & Location of test, Venue Phone Number (if applicable).

  • Take a printout of this voucher and carry it with you when appearing for your exam at the specified venue on that particular day/time window allotted by CompTIA to test centers worldwide

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Passing Score, Duration & Questions For The CompTIA DA0-001 Exam
  • Exam Length: 90 questions
  • Format: Multiple choice and performance-based
  • The passing score: 675 (on scale of 100-900)
  • Languages: English
  • Time Duration: 90 minutes
Objectives of CompTIA DA0-001 Exam

In this section, we will list the objectives you should know in order to pass your CompTIA Data+ certification exam.

The first objective is to understand how to become a successful IT professional with data skills.

The second objective is to learn about different types of data, including structured and unstructured data.

The third objective is to be able to demonstrate your knowledge of at least two types of data storage: online and offline.

The fourth objective is to be able to describe how a client can access information from various sources through technology such as social media or the web. CompTIA DA0-001 exam dumps he best way to prepare for the exams.

The fifth objective is for you to be able to apply concepts related to using software applications such as Microsoft Office suite, Google Docs, and Adobe Creative Cloud.

Reference: https://www.comptia.org/training/books/data-da0-001-study-guide

CompTIA DA0-001 Exam Syllabus Topics:
TopicDetails

Data Concepts and Environments - 15%

Identify basic concepts of data schemas and dimensions.- Databases
  • Relational
  • Non-relational

- Data mart/data warehousing/data lake

  • Online transactional processing (OLTP)
  • Online analytical processing (OLAP)

- Schema concepts

  • Snowflake
  • Star

- Slowly changing dimensions

  • Keep current information
  • Keep historical and current information
Compare and contrast different data types.- Date
- Numeric
- Alphanumeric
- Currency
- Text
- Discrete vs. continuous
- Categorical/dimension
- Images
- Audio
- Video
Compare and contrast common data structures and file formats.- Structures
  • Structured
    - Defined rows/columns
    - Key value pairs
  • Unstructured
    - Undefined fields
    - Machine data

- Data file formats

  • Text/Flat file
    - Tab delimited
    - Comma delimited
  • JavaScript Object Notation (JSON)
  • Extensible Markup Language (XML)
  • Hypertext Markup Language (HTML)

Data Mining - 25%

Explain data acquisition concepts.- Integration
  • Extract, transform, load (ETL)
  • Extract, load, transform (ELT)
  • Delta load
  • Application programming interfaces (APIs)

- Data collection methods

  • Web scraping
  • Public databases
  • Application programming interface (API)/web services
  • Survey
  • Sampling
  • Observation
Identify common reasons for cleansing and profiling datasets.- Duplicate data
- Redundant data
- Missing values
- Invalid data
- Non-parametric data
- Data outliers
- Specification mismatch
- Data type validation
Given a scenario, execute data manipulation techniques.- Recoding data
  • Numeric
  • Categorical

- Derived variables
- Data merge
- Data blending
- Concatenation
- Data append
- Imputation
- Reduction/aggregation
- Transpose
- Normalize data
- Parsing/string manipulation

Explain common techniques for data manipulation and query optimization.- Data manipulation
  • Filtering
  • Sorting
  • Date functions
  • Logical functions
  • Aggregate functions
  • System functions

- Query optimization

  • Parametrization
  • Indexing
  • Temporary table in the query set
  • Subset of records
  • Execution plan

Data Analysis - 23%

Given a scenario, apply the appropriate descriptive statistical methods.- Measures of central tendency
Mean
Median
Mode
- Measures of dispersion
  • Range
    Max
    Min
  • Distribution
  • Variance
  • Standard deviation

- Frequencies/percentages
- Percent change
- Percent difference
- Confidence intervals

Explain the purpose of inferential statistical methods.- t-tests
- Z-score
- p-values
- Chi-squared
- Hypothesis testing
  • Type I error
  • Type II error

- Simple linear regression
- Correlation

Summarize types of analysis and key analysis techniques.- Process to determine type of analysis
  • Review/refine business questions
  • Determine data needs and sources to perform analysis
  • Scoping/gap analysis

- Type of analysis

  • Trend analysis
    - Comparison of data over time
  • Performance analysis
    - Tracking measurements against defined goals
    - Basic projections to achieve goals
  • Exploratory data analysis
    - Use of descriptive statistics to determine observations
  • Link analysis
    - Connection of data points or pathway
Identify common data analytics tools.- Structured Query Language (SQL)
- Python
- Microsoft Excel
- R
- Rapid mining
- IBM Cognos
- IBM SPSS Modeler
- IBM SPSS
- SAS
- Tableau
- Power BI
- Qlik
- MicroStrategy
- BusinessObjects
- Apex
- Dataroma
- Domo
- AWS QuickSight
- Stata
- Minitab

Visualization - 23%

Given a scenario, translate business requirements to form a report.- Data content
- Filtering
- Views
- Date range
- Frequency
- Audience for report
  • Distribution list
Given a scenario, use appropriate design components for reports and dashboards.- Report cover page
  • Instructions
  • Summary
    - Observations and insights

- Design elements

  • Color schemes
  • Layout
  • Font size and style
  • Key chart elements
    - Titles
    - Labels
    - Legends
  • Corporate reporting standards/style guide
    - Branding
    - Color codes
    - Logos/trademarks
    - Watermark

- Documentation elements

  • Version number
  • Reference data sources
  • Reference dates
    - Report run date
    - Data refresh date
    - Frequently asked questions (FAQs)
    - Appendix
Given a scenario, use appropriate methods for dashboard development.- Dashboard considerations
  • Data sources and attributes
    - Field definitions
    - Dimensions
    - Measures
  • Continuous/live data feed vs. static data
  • Consumer types
    - C-level executives
    - Management
    - External vendors/stakeholders
    - General public
    - Technical experts

- Development process

  • Mockup/wireframe
    - Layout/presentation
    - Flow/navigation
    - Data story planning
  • Approval granted
  • Develop dashboard
  • Deploy to production

Delivery considerations

  • Subscription
  • Scheduled delivery
  • Interactive (drill down/roll up)
    - Saved searches
    - Filtering
    - Static
    - Web interface
    - Dashboard optimization
    - Access permissions
Given a scenario, apply the appropriate type of visualization.- Line chart
- Pie chart
- Bubble chart
- Scatter plot
- Bar chart
- Histogram
- Waterfall
- Heat map
- Geographic map
- Tree map
- Stacked chart
- Infographic
- Word cloud
Compare and contrast types of reports.- Static vs. dynamic reports
  • Point-in-time
  • Real time

- Ad-hoc/one-time report
- Self-service/on demand
- Recurring reports

  • Compliance reports (e.g., financial, health, and safety)
  • Risk and regulatory reports
  • Operational reports [e.g., performance, key performance indicators (KPIs)]

- Tactical/research report

Data Governance, Quality, and Controls - 14%

Summarize important data governance concepts.- Access requirements
  • Role-based
  • User group-based
  • Data use agreements
  • Release approvals

- Security requirements

  • Data encryption
  • Data transmission
  • De-identify data/data masking

- Storage environment requirements

  • Shared drive vs. cloud based vs. local storage

- Use requirements

  • Acceptable use policy
  • Data processing
  • Data deletion
  • Data retention

- Entity relationship requirements

  • Record link restrictions
  • Data constraints
  • Cardinality

- Data classification

  • Personally identifiable information (PII)
  • Personal health information (PHI)
  • Payment card industry (PCI)

- Jurisdiction requirements

  • Impact of industry and governmental regulations

- Data breach reporting

  • Escalate to appropriate authority
Given a scenario, apply data quality control concepts.- Circumstances to check for quality
  • Data acquisition/data source
  • Data transformation/intrahops
    - Pass through
    - Conversion
  • Data manipulation
  • Final product (report/dashboard, etc.)

- Automated validation

  • Data field to data type validation
  • Number of data points

- Data quality dimensions

  • Data consistency
  • Data accuracy
  • Data completeness
  • Data integrity
  • Data attribute limitations

- Data quality rule and metrics

  • Conformity
  • Non-conformity
  • Rows passed
  • Rows failed

- Methods to validate quality

  • Cross-validation
  • Sample/spot check
  • Reasonable expectations
  • Data profiling
  • Data audits
Explain master data management (MDM) concepts.- Processes
  • Consolidation of multiple data fields
  • Standardization of data field names
  • Data dictionary

- Circumstances for MDM

  • Mergers and acquisitions
  • Compliance with policies and regulations
  • Streamline data access
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