DA0-002 Lab Questions & DA0-002 Latest Test Simulator
DA0-002 Lab Questions & DA0-002 Latest Test Simulator
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Tags: DA0-002 Lab Questions, DA0-002 Latest Test Simulator, DA0-002 Passguide, DA0-002 Passleader Review, DA0-002 Latest Materials
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CompTIA Data+ Exam (2025) Sample Questions (Q34-Q39):
NEW QUESTION # 34
A company's analytics manager wants all reports to be delivered once every seven days. Which of the following is the best delivery method?
- A. Snapshot
- B. Recurring
- C. Custom
- D. Ad hoc
Answer: B
Explanation:
This question pertains to theVisualization and Reportingdomain, focusing on report delivery methods. The requirement for delivery every seven days indicates a scheduled, repeating process.
* Recurring (Option A): Recurring delivery schedules reports to be generated and delivered at regular intervals (e.g., weekly), which matches the requirement of every seven days.
* Ad hoc (Option B): Ad hoc reports are one-time, on-demand reports, not suitable for scheduled delivery.
* Custom (Option C): Custom isn't a standard delivery method; it might refer to tailored reports but doesn't imply scheduling.
* Snapshot (Option D): A snapshot captures data at a specific point, not suitable for recurring delivery.
The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and recurring delivery is ideal for weekly reports.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 35
A data analyst is joining two tables with different content and one common field. Which of the following should the analyst do to most efficiently meet this requirement?
- A. Explode both tables to identify unique values and reorder the fields in one table.
- B. Create a cluster to facilitate data integration between the tables.
- C. Append the values of the matching columns and concatenate the other data fields.
- D. Match the records of the related columns and merge the tables.
Answer: D
Explanation:
This question falls under theData Acquisition and Preparationdomain, focusing on combining data from multiple tables. The tables have different content but share a common field, indicating a join operation.
* Match the records of the related columns and merge the tables (Option A): This describes a join operation, where records are matched on the common field (e.g., a key like Customer_ID) and the tables are merged, which is the most efficient method.
* Create a cluster to facilitate data integration between the tables (Option B): Clustering is a machine learning technique, not a method for joining tables.
* Explode both tables to identify unique values and reorder the fields in one table (Option C):
Exploding is used in nested data (e.g., JSON arrays), and this approach is overly complex and unnecessary.
* Append the values of the matching columns and concatenate the other data fields (Option D):
Appending stacks tables vertically, and concatenation applies to text, neither of which is appropriate for joining tables with a common field.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as joining tables using a common field.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.
NEW QUESTION # 36
Which of the following tables holds relational keys and numeric values?
- A. Dimensional
- B. Fact
- C. Transactional
- D. Graph
Answer: B
Explanation:
This question falls under theData Concepts and Environmentsdomain, focusing on understanding table types in data warehousing. The task is to identify a table that holds relational keys and numeric values, typically used in a star schema.
* Fact (Option A): Fact tables in a star schema store quantitative data (numeric values, e.g., sales amounts) and foreign keys (relational keys) linking to dimension tables, making this the correct choice.
* Graph (Option B): Graph tables are used in graph databases for relationships (e.g., nodes, edges), not typically for relational keys and numeric values in a traditional sense.
* Dimensional (Option C): Dimension tables store descriptive attributes (e.g., product names) and primary keys, not typically numeric measures.
* Transactional (Option D): Transactional tables are used in OLTP systems and may contain numeric values, but they're not specifically designed for relational keys in a data warehousing context.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and fact tables are designed to hold relational keys and numeric values in a data warehouse.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
NEW QUESTION # 37
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?
- A. Mean and median
- B. Standard deviation and constraints
- C. Boolean data and an array
- D. Numerical and ordinal attributes
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 # 38
A data analyst creates a report that identifies the middle 50% of the collected data. Which of the following best describes the analyst's findings?
- A. Interquartile range
- B. Mean variance
- C. Skewness from the slope
- D. The difference between mode and median
Answer: A
Explanation:
This question pertains to theData Analysisdomain, focusing on statistical measures. The middle 50% of a dataset refers to a specific statistical concept related to data distribution.
* Interquartile range (Option A): The interquartile range (IQR) is the range between the first quartile (Q1, 25th percentile) and the third quartile (Q3, 75th percentile), representing the middle 50% of the data, which matches the description.
* The difference between mode and median (Option B): This measures the spread between two central tendency metrics but doesn't represent the middle 50% of the data.
* Mean variance (Option C): Variance measures data dispersion around the mean, not the middle 50%.
* Skewness from the slope (Option D): Skewness measures data asymmetry, and "slope" is irrelevant here.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and the IQR is the standard measure for the middle 50% of a dataset.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 39
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