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| Certification Vendor: | CompTIA |
|---|---|
| Exam Name: | CompTIA Data+ Certification Exam |
| Exam Number: | DA0-002 |
| Certificate Validity Period: | 3 years |
| Available Languages: | English |
| Exam Price: | $253 USD (may vary by region/tax) |
| Real Exam Qty: | Up to 90 |
| Exam Duration: | 90 minutes |
| Passing Score: | 675 (on a scale of 100โ900) |
| Exam Format: | Performance-based questions, Multiple-choice, Multiple-response |
| Recommended Training: | CompTIA CertMaster Learn for Data+ CompTIA Data+ Study Resources |
| Exam Registration: | Pearson VUE CompTIA Exams Registration CompTIA Data+ Certification Page |
| Sample Questions: | CompTIA DA0-002 Sample Questions |
| Exam Way: | Delivered via Pearson VUE testing centers or online proctored exam |
| Pre Condition: | No mandatory prerequisites. Recommended: 18โ24 months of experience in a business/data analytics environment. |
| Official Syllabus URL: | https://www.comptia.org/certifications/data-plus |
>> Current DA0-002 Exam Content <<
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NEW QUESTION # 124
An analyst must use the same data to create reports for police patrol supervisors, the city council, and a public website. Which of the following is the best way to differentiate the reports?
Answer: A
Explanation:
This question falls under theVisualization and Reportingdomain, focusing on tailoring reports for different audiences. The same data is used for three distinct groups (supervisors, city council, public), requiring differentiation.
* Persona type (Option A): Persona types define the audience's needs and preferences (e.g., supervisors need operational details, the city council needs summaries, the public needs simplified data), making this the best way to differentiate the reports.
* Detail levels (Option B): Detail levels are a result of persona types, not the method of differentiation.
* Accessibility (Option C): Accessibility ensures access for all users (e.g., screen readers), but it's not the primary way to differentiate content.
* Sensitivity (Option D): Sensitivity determines data access (e.g., confidential vs. public), but the scenario implies all reports use the same data, just presented differently.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and persona types guide report differentiation for diverse audiences.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 125
Which of the following is found in metadata?
Answer: A
Explanation:
This question pertains to theData Concepts and Environmentsdomain, focusing on the content of metadata.
Metadata describes data attributes, and the task is to identify what it typically includes.
* Transformations (Option A): Transformations (e.g., data cleaning steps) are part of data lineage, not metadata.
* Data lineage (Option B): Data lineage tracks data flow and transformations, which is related to metadata but not a direct component.
* Syntax (Option C): Syntax refers to code structure, not a metadata component.
* Variable types (Option D): Metadata includes information about data fields, such as variable types (e.
g., integer, string), which is a standard component.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and metadata typically contains details like variable types to describe the dataset.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
NEW QUESTION # 126
A data analyst wants to use the following tables to find all the customers who have not placed an order:
Customers table
ID
Name
Address
Products table
ID
Name
Customer_ID
Which of the following SQL statements is the best way to accomplish this task?
Answer: D
Explanation:
This question pertains to theData Analysisdomain, focusing on SQL queries to analyze data relationships.
The task is to find customers who have not placed an order, meaning customers in the Customers table without a matching Customer_ID in the Products table.
* Option A: SELECT * FROM CUSTOMERS AS C LEFT JOIN PRODUCTS AS P ON C.ID = P.
Customer_ID WHERE P.Customer_ID IS NULLA LEFT JOIN includes all customers, even those without orders (where Products columns are NULL). Filtering with WHERE P.Customer_ID IS NULL selects only customers without a match in Products, correctly identifying those who haven't ordered.
* Option B: SELECT * FROM CUSTOMERS AS C INNER JOIN PRODUCTS AS P ON C.ID = C.
ID WHERE COUNT(P.*) = 0An INNER JOIN only includes matching records, so it won't return customers without orders. The join condition C.ID = C.ID is also incorrect, and COUNT requires a GROUP BY, making this invalid.
* Option C: SELECT * FROM PRODUCTS AS P INNER JOIN CUSTOMERS AS C ON P.
Customer_ID = C.ID WHERE (SELECT COUNT(P.*) = 0)An INNER JOIN excludes customers without orders, and the subquery syntax is incorrect (COUNT needs a GROUP BY or to be part of a HAVING clause).
* Option D: SELECT * FROM PRODUCTS AS P LEFT JOIN CUSTOMERS AS C ON P.
Customer_ID = C.ID WHERE P.Customer_ID IS NOT NULLThis starts with Products and joins Customers, returning only records with orders (opposite of the task), and IS NOT NULL further excludes non-ordering customers.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and a LEFT JOIN with a NULL check is the standard method for finding non-matching records.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 127
Before distributing a report, a marketing analyst notices that the total distinct promotional email messages is less than the combined total of emails sent. Which of the following is the most likely reason for this difference?
Answer: A
Explanation:
This question falls under theData Analysisdomain, focusing on analyzing discrepancies in data reports. The total distinct messages are fewer than the total emails sent, indicating a specific issue.
* The aggregation did not include all emails (Option A): If the aggregation missed emails, the total sent would be lower, not the distinct count.
* Some emails were not delivered (Option B): Undelivered emails would reduce the total sent, but the scenario implies the total sent is accurate.
* The report failed to run properly (Option C): A report failure would likely cause broader issues, not a specific discrepancy between distinct and total counts.
* A recipient received duplicate emails (Option D): If recipients received duplicates, the total emails sent would be higher than the distinct messages (unique email content), explaining the difference.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and identifying duplicates is a common analysis task to explain such discrepancies.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 128
A data analyst is working on an initial analysis of the dataset in the following table:
DateTime
Count
2024-01-01
12
2024-01-02
245
2024-01-02
13
2024-01-03
13
2024-01-03
12
00:00:00
12
Which of the following issues should the analyst flag in the data report?
Answer: D
Explanation:
This question falls under theData Analysisdomain, focusing on identifying data quality issues.The table shows counts over time, and the analyst needs to flag an issue in the data.
* Completeness (Option A): Completeness refers to missing data, but all rows have values for DateTime and Count.
* Outlier (Option B): The count of 245 on 2024-01-02 is significantly higher than other counts (12-13), indicating an outlier that should be investigated for accuracy.
* Mismatch (Option C): Mismatch implies inconsistent data types or formats, but the DateTime and Count columns appear consistent except for the last row (addressed separately).
* Duplication (Option D): Duplication refers to identical rows, but no rows are identical (same DateTime and Count).
The last row ("00:00:00", 12) has a formatting issue, but the most significant issue for analysis is the outlier (245), as it could skew results. The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," such as identifying outliers in datasets.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 129
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