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

  • Exam Code: DA0-001J
  • Exam Name: CompTIA Data+ Certification Exam (DA0-001日本語版)
  • Updated: Jun 02, 2026
  • Q&As: 398 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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Is The CompTIA DA0-001 Exam Worth It

CompTIA Data+ exam is worth it, but only if you are looking for a career in IT. If you are just looking for a career, then it is not worth it. The CompTIA Data+ certification is not about learning how to use the computer, it is about understanding the system and how to use it effectively. It takes many years of training and experience to become proficient at using this type of software.

The CompTIA Data+ exam is designed to screen out candidates who have no concept of what they are doing when they work with computers. It requires that you have some knowledge of what the different parts of a computer do, how they work together and how they interact with each other. It also requires that you have some basic familiarity with networking systems and networks in general as well as knowledge about security issues related to computers and networks in general. CompTIA DA0-001 exam dumps are the key of success.

The CompTIA Data+ certification is an excellent starting point for anyone who wants to learn more about IT but does not have any formal training or education in this area yet. It can serve as a great entry point into the world of technology if you have already been exposed to computers but don't know much about them.

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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Things You Should Know about the CompTIA DA0-001 Exam

CompTIA Data+ is a certification that validates your understanding of the fundamentals of computer data storage and management. It's a foundational certification that can be used to secure entry-level positions in various fields, such as IT, business intelligence, and application development.

CompTIA Data+ is not the only certification available for this subject matter. The CompTIA A+ and Microsoft Certified Solutions Associate exams also cover similar material. However, unlike those exams, CompTIA D+ does not have a pre-requisite requirement for passing it. If you already have a working knowledge of networking, operating systems (OS), and some basic computer concepts, you can prepare for this exam without any prior experience in these areas. CompTIA DA0-001 exam dumps will help you to clear the concepts.

CompTIA Data+ is designed to ensure that candidates have a thorough understanding of how data storage works and the basic concepts involved in managing it. This includes an understanding of how information is stored electronically on hard drives, solid-state drives (SSDs), magnetic tapes, optical discs and other storage devices; along with how data can be retrieved from these devices using appropriate software tools.

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

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