RELIABLE DA0-001 DUMPS PPT, DA0-001 HIGH PASSING SCORE

Reliable DA0-001 Dumps Ppt, DA0-001 High Passing Score

Reliable DA0-001 Dumps Ppt, DA0-001 High Passing Score

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Tags: Reliable DA0-001 Dumps Ppt, DA0-001 High Passing Score, 100% DA0-001 Correct Answers, Valid DA0-001 Exam Cram, DA0-001 Study Dumps

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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%


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CompTIA DA0-001 High Passing Score & 100% DA0-001 Correct Answers

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CompTIA DA0-001 exam covers a wide range of topics, including database concepts, data modeling, data warehousing, data visualization, and data security. DA0-001 exam is designed to test the practical knowledge of candidates in these areas and their ability to apply this knowledge in real-world scenarios. DA0-001 Exam consists of 90 multiple-choice questions, and candidates have 90 minutes to complete it.

CompTIA Data+ Certification Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
Which of the following is an example of a discrete variable?

  • A. The temperature of a hot tub
  • B. The number of people in an office
  • C. The time to complete a task
  • D. The height of a horse

Answer: B

Explanation:
Explanation
A discrete variable is a variable that can only take on a finite number of values, such as integers or categories.
The number of people in an office is an example of a discrete variable, as it can only be a whole number. The temperature of a hot tub, the height of a horse, and the time to complete a task are examples of continuous variables, as they can take on any value within a range. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy


NEW QUESTION # 60
A data analyst has been asked to derive a new variable labeled "Promotion_flag" based on the total quantity sold by each salesperson. Given the table below:

Which of the following functions would the analyst consider appropriate to flag "Yes" for every salesperson who has a number above 1,000,000 in the Quantity_sold column?

  • A. Date
  • B. Aggregate
  • C. Mathematical
  • D. Logical

Answer: D

Explanation:
A logical function is a type of function that returns a value based on a condition or a set of conditions. For example, the IF function in Excel can be used to check if a certain condition is met, and then return one value if true, and another value if false. In this case, the data analyst can use a logical function to check if the Quantity_sold column is greater than 1,000,000, and then return "Yes" if true, and "No" if false. This would create a new variable called Promotion_flag that indicates whether the salesperson has sold more than
1,000,000 units or not. References: CompTIA Data+ Certification Exam Objectives, Logical functions (reference)


NEW QUESTION # 61
Given the table below:

Which of the following variables can be considered inconsistent, and how many distinct values should the variable have?

  • A. Name, one
  • B. Region, five
  • C. Gender, two
  • D. Code, four
  • E. Level, three

Answer: C


NEW QUESTION # 62
Which of the following descriptive statistical methods are measures of central tendency? (Choose two.)

  • A. Variance
  • B. Mode
  • C. Maximum
  • D. Minimum
  • E. Mean
  • F. Correlation

Answer: B,E

Explanation:
Mean and mode are measures of central tendency, which describe the typical or most common value in a distribution of data. Mean is the arithmetic average of all the values in a dataset, calculated by adding up all the values and dividing by the number of values. Mode is the most frequently occurring value in a dataset. Other measures of central tendency include median, which is the middle value when the data is sorted in ascending or descending order.


NEW QUESTION # 63
Which of the following is a common data analytics tool that is also used as an interpreted, high-level, general-purpose programming language?

  • A. Python
  • B. SAS
  • C. Microsoft Power BI
  • D. IBM SPSS

Answer: A

Explanation:
Python is a common data analytics tool that is also used as an interpreted, high-level, general-purpose programming language. Python has a simple and expressive syntax that makes it easy to read and write code. Python also has a rich set of libraries and frameworks that support various tasks and applications in data analytics, such as data manipulation, visualization, machine learning, natural language processing, web scraping, and more. Some examples of popular Python libraries for data analytics are pandas, numpy, matplotlib, seaborn, scikit-learn, nltk, and beautifulsoup. Python is different from other data analytics tools that are not programming languages but rather software applications or platforms that provide graphical user interfaces (GUIs) for data analysis and visualization. Some examples of these tools are SAS, Microsoft Power BI, IBM SPSS. Therefore, the correct answer is D. Reference: [What is Python? | Definition and Examples], [Python Libraries for Data Science]


NEW QUESTION # 64
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