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

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

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

3. 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).

4. 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).

5. 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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CompTIA DA0-001 Exam Syllabus Topics:

Topic
Details

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

The CompTIA Data+ Certification Exam certification exam is intended for professionals who work in various fields, including IT, finance, healthcare, and government. It is also ideal for individuals who want to enhance their career opportunities in the field of data management. CompTIA Data+ Certification Exam certification is recognized globally, which means that individuals who hold this certification can find job opportunities in various countries around the world.

CompTIA Data+ Certification Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
Which of the following is the best reason for removing data outliers?

• A. Data is missing from the table.
• B. Data is redundant in the table.
• C. Data is duplicated in the whole range.
• D. Data varies significantly from others.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation:
Data outliers are observations that deviate markedly from other observations in the dataset. Handling outliers appropriately is crucial in data analysis to ensure the accuracy and reliability of insights derived from the data.
Option A:Data varies significantly from others.
* Rationale:Outliers are data points that differ significantly from other observations. They can skew statistical analyses, leading to misleading results. Removing or addressing outliers can help in achieving a more accurate representation of the data, ensuring that analyses and models are not unduly influenced by anomalous values.

NEW QUESTION # 73
An e-commerce company recently tested a new website layout. The website was tested by a test group of customers, and an old website was presented to a control group. The table below shows the percentage of users in each group who made purchases on the websites:

Which of the following conclusions is accurate at a 95% confidence interval?

• A. The new layout has the lowest conversion rates in the United Kingdom.
• B. In France, the increase in conversion from the new layout was not significant.
• C. In Germany, the increase in conversion from the new layout was not significant.
• D. In general, users who visit the new website are more likely to make a purchase.

Answer: C

Explanation:
Explanation
The p-value is a measure of how likely it is to observe a difference in conversion rates as large or larger than the one observed, assuming that there is no difference between the groups. A common threshold for statistical significance is 0.05, meaning that there is a 5% or less chance of observing such a difference by chance alone.
The table shows the p-values for each country, and we can see that only Germany has a p-value above 0.05 (0.13). This means that we cannot reject the null hypothesis that there is no difference in conversion rates between the test and control groups in Germany. Therefore, the increase in conversion from the new layout was not significant in Germany. For the other countries, the p-values are below 0.05, indicating that the increase in conversion from the new layout was statistically significant. Option A is correct.
Option B is incorrect because the increase in conversion from the new layout was significant in France (p-value = 0.002).
Option C is incorrect because it does not account for the variation across countries. While the overall conversion rate for the test group (8.4%) is higher than the control group (6.8%), this difference may not be statistically significant when we consider the country-specific effects.
Option D is incorrect because the new layout has the highest conversion rate in the United Kingdom (9.6%), not the lowest.
References:
P-value Calculator & Statistical Significance Calculator
p-value Calculator | Formula | Interpretation
How to obtain the P value from a confidence interval | The BMJ
Confidence Intervals & P-values for Percent Change / Relative Difference

NEW QUESTION # 74
An analyst for a small business with multiple locations is using each location's quarterly sales reports from last year to create a single revenue report for the year. Which of the following data mining techniques should the analyst use to complete this task?

• A. Data append
• B. Data merge
• C. Data blending
• D. Data imputation

Answer: B

Explanation:
Comprehensive and Detailed In-Depth
To compile a comprehensive annual revenue report from multiple quarterly sales reports, the analyst should integrate the datasets appropriately:
Data Merge: This technique involves combining datasets based on common fields or keys. In this scenario, merging would align data from different quarters based on shared attributes, such as location identifiers, to create a unified dataset.
Data Append: Appending adds datasets sequentially, stacking them on top of each other. While this could combine the reports, it doesn't ensure integration based on common fields, which is necessary for accurate analysis.
Data Blending: Blending combines data from different sources or formats. If the quarterly reports are in varied formats or from different systems, blending would be appropriate. However, if they share the same structure, blending isn't necessary.
Data Imputation: Imputation addresses missing or incomplete data by filling in gaps. This technique isn't relevant to combining complete quarterly reports.
Therefore, merging the data ensures that all quarterly reports are integrated based on common fields, providing a cohesive annual revenue report.

NEW QUESTION # 75
What three technological innovations contribute to modern analytics?
Choose three answers.

• A. Computing.
• B. Data.
• C. Storage.
• D. Dashboard.

Answer: A,B,C

NEW QUESTION # 76
Which of the following is the correct data type for text?

• A. Boolean
• B. Integer
• C. Float
• D. String

Answer: D

Explanation:
Explanation
A string is a data type that represents a sequence of characters, such as text, symbols, numbers, or punctuation marks. Strings are enclosed in quotation marks, such as "Hello", "123", or "!@#". Strings can be manipulated, concatenated, sliced, indexed, formatted, and searched using various methods and functions. A string is different from other data types, such as boolean, integer, or float, which represent logical values (true or false), whole numbers, or decimal numbers respectively. Therefore, the correct answer is B. References: What is a String? | Definition and Examples, Python String Methods

NEW QUESTION # 77
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