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| Certification Vendor: | CompTIA |
|---|---|
| Exam Name: | CompTIA Data+ Exam |
| Exam Number: | DA0-002 |
| Certificate Validity Period: | 3 years |
| Exam Format: | Performance-based questions, Multiple-choice |
| Available Languages: | English |
| Real Exam Qty: | Up to 90 |
| Exam Price: | $255 USD |
| Exam Duration: | 90 minutes |
| Passing Score: | 720 (scale 100โ900) |
| Recommended Training: | CompTIA Official Training |
| Exam Registration: | Pearson VUE Testing CompTIA Official Registration |
| Sample Questions: | CompTIA DA0-002 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | Recommended 18โ24 months of experience in data/business analysis role; familiarity with databases, analytical tools, basic statistics, and visualization |
| Official Syllabus URL: | https://www.comptia.org/certifications/data |
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NEW QUESTION # 12
A data analyst receives the following sales data for a convenience store:
Item
Quantity
Price
Chocolate Bars
7
$1.99
Vanilla Ice Bars
2
$4.99
Chocolate Wafers
6
$0.99
Peanut Butter
2
$2.99
Cups
3
$4.99
Strawberry Jam
3
$4.99
Chocolate Cake
9
$6.99
Milk Chocolate
2
$2.99
Almonds
5
$2.99
The analyst needs to provide information on the products that contain chocolate. Which of the following RegEx should the analyst use to filter the chocolate products?
Answer: C
Explanation:
This question falls under theData Acquisition and Preparationdomain, which includes techniques for manipulating and filtering data, such as using regular expressions (RegEx) to identify specific patterns in text data. The task is to filter items containing the word "Chocolate."
* Chocolate! (Option A): In RegEx, "!" is not a valid pattern for matching a word like "Chocolate." It typically denotes negation in some contexts, but here it's incorrect.
* Chocolate$ (Option B): The "$" in RegEx anchors the pattern to the end of the string, meaning it matches "Chocolate" at the end of an item name (e.g., "Milk Chocolate"). This is the most appropriate pattern for identifying items ending with "Chocolate," which applies to the relevant items in the list.
* %Chocolate& (Option C): "%" and "&" are not standard RegEx anchors; they're often used in SQL LIKE patterns, not RegEx, making this incorrect.
* #Chocolate#$ (Option D): "#" is not a standard RegEx anchor, and this pattern would look for
"Chocolate" surrounded by "#", which doesn't match the data.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation" , and RegEx is a common technique for filtering text data. The pattern "Chocolate$" correctly identifies items like
"Chocolate Bars," "Chocolate Wafers," "Chocolate Cake," and "Milk Chocolate." Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation
NEW QUESTION # 13
A data analyst needs to create a combined report that includes information from the following two tables:
Managers table
ID
First_name
Last_name
Job_title
1001
John
Doe
Manager
1002
Jane
Roe
Director
Non-managers table
ID
First_name
Last_name
Job_title
1003
Robert
Roe
Business Analyst
1004
Jane
Doe
Sales Representative
1005
John
Roe
Operations Analyst
Which of the following query methods should the analyst use for this task?
Answer: D
Explanation:
This question pertains to theData Acquisition and Preparationdomain, focusing on combining data from two tables. Both tables have the same structure (ID, First_name, Last_name, Job_title) and contain employee data, suggesting the task is to create a single list of all employees.
* Group (Option A): Grouping (e.g., GROUP BY in SQL) is for aggregation (e.g., counting employees by job title), not combining tables into a single report.
* Join (Option B): Joining tables (e.g., INNER JOIN) requires a common key and combines tables horizontally, but there's no indication of a relationship between the tables (e.g., no shared key beyond ID, which isn't linked).
* Union (Option C): UNION combines the rows of two tables with the same structure into a single result set, removing duplicates, which is ideal for creating a combined report of all employees from both tables.
* Nested (Option D): Nested queries (e.g., subqueries) are used for complex filtering, not for combining tables into a single list.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and UNION is the best method for combining two tables with identical structures into a single report.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.
NEW QUESTION # 14
A data analyst needs to derive a new field that flags sales within the month of January from the following sales table:
Which of the following types of functions should the analyst use to achieve the desired results?
Answer: D
Explanation:
Logical functions evaluate conditions such as whether the month equals January and can be used to create a flag indicating records that meet this criterion.
NEW QUESTION # 15
Which of the following elements is the most important to include in a dashboard for internal technical audiences?
Answer: A
Explanation:
This question pertains to theVisualization and Reportingdomain, focusing on dashboard design for specific audiences. Internal technical audiences (e.g., data analysts, IT staff) need actionable, data-driven insights.
* Methodology section (Option A): Methodology is important for research reports, not dashboards, especially for technical audiences who prioritize data.
* Dynamic features (Option B): Dynamic features (e.g., interactivity) are useful but not the most critical element for technical audiences.
* Key performance indicators (Option C): KPIs provide critical metrics (e.g., system uptime, error rates) that technical audiences need to monitor and act on, making this the most important element.
* Company branding (Option D): Branding is more relevant for external audiences, not internal technical ones.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and KPIs are essential for technical dashboards.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 16
A data analyst is creating a forecast for a product line introduced early last year. Which of the following should the analyst use to create projected sales and customer satisfaction for next year?
Answer: D
Explanation:
This question pertains to theData Analysisdomain, focusing on data types and methods for forecasting. The task involves projecting sales (numerical) and customer satisfaction (likely ordinal,e.g., ratings), requiring appropriate data attributes.
* Standard deviation and constraints (Option A): Standard deviation measures data spread, and constraints are conditions, neither of which directly supports forecasting.
* Mean and median (Option B): Mean and median are descriptive statistics, not sufficient for forecasting future values.
* Boolean data and an array (Option C): Boolean data (true/false) and arrays (data structures) are not relevant for forecasting sales and satisfaction.
* Numerical and ordinal attributes (Option D): Sales are numerical (e.g., units sold), and customer satisfaction is often ordinal (e.g., 1-5 ratings). These attributes are suitable for forecasting models (e.g., time-series analysis for sales, regression for satisfaction).
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and numerical and ordinal attributes are key for forecasting sales and satisfaction.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 17
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