Free CompTIA DA0-002 Study Guides Exam Questions and Answer
DA0-002 Exam Dumps, DA0-002 Practice Test Questions
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NEW QUESTION # 36
A table contains several rows of cellular numbers with call timestamps, call durations, called numbers, and carriers of the called number. Which of the following allows a data analyst to sort the cellular numbers based on the carriers of the called numbers and include the total call durations?
- A. SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY call_duration ORDER BY called_number_carrier;
- B. SELECT cellular_number, SUM(call_duration) FROM calls GROUP BY call_duration ORDERBY called_number_carrier;
- C. SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY cellular_number, called_number_carrier ORDER BY called_number_carrier;
- D. SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY cellular_number ORDER BY called_number_carrier;
Answer: C
Explanation:
This question falls under theData Analysisdomain of CompTIA Data+ DA0-002, focusing on SQL queries for data analysis. The task requires sorting cellular numbers by the carrier of the called number (called_number_carrier) and calculating the total call durations (SUM(call_duration)).
* Option A: SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY cellular_number ORDER BY called_number_carrierThis query groups by cellular_number only, but called_number_carrier is in the SELECT clause without being in the GROUP BY, which is invalid in SQL (it would raise an error in most databases).
* Option B: SELECT cellular_number, SUM(call_duration) FROM calls GROUP BY call_duration ORDER BY called_number_carrierThis query doesn't include called_number_carrier in the SELECT clause, so it cannot be used in the ORDER BY clause, making it invalid. Grouping by call_duration also doesn't align with the task.
* Option C: SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY cellular_number, called_number_carrier ORDER BY called_number_carrierThis query correctly groups by both cellular_number and called_number_carrier (since both are in the SELECT clause), calculates the total call duration with SUM(call_duration), and sorts by called_number_carrier as required.
* Option D: SELECT cellular_number, called_number_carrier, SUM(call_duration) FROM calls GROUP BY call_duration ORDER BY called_number_carrierGrouping by call_duration is incorrect because cellular_number and called_number_carrier are in the SELECT clause but not in the GROUP BY, making this query invalid.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and Option C correctly aggregates and sorts the data as specified.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 37
Which of the following is the best language to use in a notebook?
- A. Haskell
- B. Python
- C. SAS
- D. JavaScript
Answer: B
NEW QUESTION # 38
A data analyst is evaluating all conditions in a query. Which of the following is the best logical function to accomplish this task?
- A. AND
- B. OR
- C. IF
- D. NOT
Answer: A
Explanation:
This question falls under theData Analysisdomain, focusing on SQL logical functions for query evaluation.
The task is to evaluate "all conditions," implying multiple conditions must be true together.
* OR (Option A): OR returns true if any condition is true, not ensuring all conditions are met.
* NOT (Option B): NOT negates a condition, not suitable for combining multiple conditions.
* AND (Option C): AND requires all conditions to be true, which aligns with evaluating "allconditions" in a query.
* IF (Option D): IF is a conditional function for decision-making, not for evaluating multiple conditions together.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and AND is the best logical function for ensuring all conditions are met.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 39
A data analyst is analyzing the following dataset:
Transaction Date
Quantity
Item
Item Price
12/12/12
11
USB Cords
9.99
11/11/11
3
Charging Block
8.89
10/10/10
5
Headphones
50.15
Which of the following methods should the analyst use to determine the total cost for each transaction?
- A. Compressing
- B. Deriving
- C. Scaling
- D. Parsing
Answer: B
Explanation:
This question falls under theData Analysisdomain, focusing on calculating new values from existing data.
The task is to determine the total cost per transaction, which involves multiplying Quantity by Item Price.
* Parsing (Option A): Parsing involves breaking down data (e.g., splitting a string), not calculating totals.
* Scaling (Option B): Scaling adjusts numerical values to a common range (e.g., normalization), not relevant for calculating totals.
* Compressing (Option C): Compressing reduces data size, not applicable to calculating costs.
* Deriving (Option D): Deriving involves creating new data fields by performing calculations on existing ones (e.g., Total Cost = Quantity × Item Price), which fits the task.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," such as deriving new fields through calculations to analyze data.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 40
Which of the following AI types is the best option for time-series forecasting?
- A. Robotic process automation
- B. Foundational models
- C. Generative AI
- D. Natural language processing
Answer: B
Explanation:
Foundational models are large AI models trained on vast amounts of data, often exhibiting strong generalization capabilities. While not specifically architected for time-series, their ability to learn complex patterns could potentially be leveraged for forecasting tasks through fine-tuning or specialized architectures built upon them.
In reality, the best AI types specifically designed for time-series forecasting include:
* Recurrent Neural Networks (RNNs), especially LSTMs and GRUs:These architectures are designed to handle sequential data and capture temporal dependencies.
* Transformer Networks:Originally developed for NLP, Transformers have shown remarkable success in time-series forecasting due to their ability to capture long-range dependencies.
* Traditional statistical models:ARIMA, Exponential Smoothing, and other statistical methods remain powerful and interpretable options for time-series analysis.
Therefore, while "foundational models" have some potential, it's important to understand that they aren't the primary or specifically designed AI type for time-series forecasting.
NEW QUESTION # 41
A data analyst needs to join together a table data source and web API data source using Python. Which of the following is the best way to accomplish this task?
- A. Convert the data from the API and database to a TXT format and convert them to pandas DataFrames that are then merged together.
- B. Convert the data from the API and database to a varchar format and convert them to pandas DataFrames that are then merged together.
- C. Convert the data from the API and database to a JSON format and convert them to pandas DataFrames that are then merged together.
- D. Convert the data from the API and database to a string format and convert them to pandas DataFrames that are then merged together.
Answer: C
NEW QUESTION # 42
A data analyst is creating a pivot table for a large dataset for an upcoming board meeting. Which of the following is the purpose of the pivot table?
- A. To visualize the data in a dashboard
- B. To retrieve and clean data from several sources
- C. To summarize and analyze the data
- D. To organize the data for reporting
Answer: C
Explanation:
This question pertains to theData Analysisdomain, focusing on the purpose of a pivot table. Pivot tables are a tool for summarizing and analyzing data, often used in preparation for reporting.
* To visualize the data in a dashboard (Option A): Pivot tables summarize data but aren' tvisualizations; charts in dashboards might be created from pivot tables.
* To retrieve and clean data from several sources (Option B): Retrieving and cleaning data is part of data preparation, not the purpose of a pivot table.
* To summarize and analyze the data (Option C): Pivot tables aggregate and summarize data (e.g., by calculating sums, averages) and allow for analysis (e.g., filtering, grouping), which is their primary purpose.
* To organize the data for reporting (Option D): While pivot tables can help organize data, their main purpose is summarization and analysis, not just organization.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and pivot tables are a key tool for summarizing and analyzing large datasets.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 43
A data analyst needs to provide a weekly sales report for the Chief Financial Officer. Which of the following delivery methods is the most appropriate?
- A. A detailed text document
- B. A high-level email
- C. A granular daily report in a dashboard
- D. A spreadsheet with raw data
Answer: B
Explanation:
This question pertains to theVisualization and Reportingdomain, focusing on report delivery methods for a specific audience. The Chief Financial Officer (CFO) needs a weekly sales report,suggesting a concise, executive-level summary.
* A granular daily report in a dashboard (Option A): Daily granularity is too frequent for a weekly report, and a dashboard might be too interactive for a CFO's needs.
* A detailed text document (Option B): A detailed document is too lengthy for a CFO, who typically needs a summary.
* A spreadsheet with raw data (Option C): Raw data requires further analysis, which isn't appropriate for an executive-level report.
* A high-level email (Option D): A high-level email provides a concise summary of weekly sales, tailored for an executive like a CFO, making it the most appropriate delivery method.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a high-level email is best for delivering a weekly summary to a CFO.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 44
Given the following table:
ID
Value
1
1.5
2
24.456
3
113
Which of the following data types should an analyst use for the numeric values in the Value column?
- A. Integer
- B. Double
- C. Boolean
- D. Float
Answer: D
Explanation:
This question falls under theData Concepts and Environmentsdomain of CompTIA Data+ DA0-002, focusing on selecting appropriate data types for a given dataset. The Value column contains decimal numbers (1.5, 24.456, 113), requiring a data type that supports such values.
* Double (Option A): Double is a floating-point data type that supports decimals with higher precision than Float, but it's often overkill for typical datasets unless very high precision is needed, which isn't indicated here.
* Float (Option B): Float is a floating-point data type that supports decimal numbers (e.g., 1.5, 24.456) and is commonly used for such values in databases, making it the best choice.
* Boolean (Option C): Boolean is for true/false values, not numeric data.
* Integer (Option D): Integer is for whole numbers, but the values (e.g., 1.5, 24.456) have decimals, so Integer is not suitable.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as selecting data types like Float for decimal numeric values.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
NEW QUESTION # 45
A data analyst is creating a new dataset that involves bringing together the following datasets:
Name
ID
Date of birth
Frank
23525
3/19
Martha
11290
6/13
Ellen
12141
11/4
ID
Address
City
State
23525
1234 Harding
Chicago
IL
11040
935 Terrace Hills
Chino
CA
11290
2 Speedway
Miami
FL
Which of the following would be the output if the data analyst does a FULL JOIN?
- A. Name
ID
Date of birth
Address
City
State
Frank
23525
3/19
1234 Harding
Chicago
IL
Martha
11290
6/13
935 Terrace Hills
Chino
CA
Ellen
12141
11/4
2 Speedway
Miami
FL - B. Name
ID
Date of birth
Address
City
State
Frank
23525
3/19
1234 Harding
Chicago
IL
Martha
11290
6/13
2 Speedway
Miami
FL
Ellen
12141
11/4
935 Terrace Hills
Chino
CA - C. Name
ID
Date of birth
Address
City
State
Frank
23525
3/19
1234 Harding
Chicago
IL
Martha
11290
6/13
2 Speedway
Miami
FL
Ellen
12141
11/4
11040
935 Terrace Hills
Chino
CA - D. Name
ID
Date of birth
Address
City
State
Frank
23525
3/19
1234 Harding
Chicago
IL
Martha
11290
6/13
935 Terrace Hills
Chino
CA
Ellen
12141
11/4
2 Speedway
Miami
FL
Answer: C
Explanation:
This question falls under theData Concepts and Environmentsdomain, focusing on database operations like joins. A FULL JOIN combines all rows from both tables, including matches and non-matches, filling in NULLs where there's no corresponding data.
* The first table has IDs: 23525 (Frank), 11290 (Martha), 12141 (Ellen).
* The second table has IDs: 23525, 11040, 11290.
* A FULL JOIN includes all IDs: 23525, 11290, 12141, 11040.
* 23525 matches (Frank with 1234 Harding, Chicago, IL).
* 11290 matches (Martha with 2 Speedway, Miami, FL).
* 12141 has no match in the second table, so Address, City, and State are NULL.
* 11040 has no match in the first table, so Name and Date of birth are NULL.
* Option A: Incorrect; it includes a row for Ellen with "2 Speedway," but Ellen's ID (12141) doesn't match any address, and 11040 is missing.
* Option B: Identical to Option A, so incorrect for the same reasons.
* Option C: Incorrect; it mismatches addresses (e.g., Ellen with 935 Terrace Hills, which belongs to
11040).
* Option D: Correct; it includes all IDs, with NULLs for non-matching rows (Ellen has no address, and
11040 has no name).
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as performing joins in relational databases.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
NEW QUESTION # 46
Which of the following is found in metadata?
- A. Data lineage
- B. Syntax
- C. Variable types
- D. Transformations
Answer: C
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 # 47
A data analyst creates a report, and some of the fields are empty. Which of the following conditions should the analyst add to a query to provide a list of all the records with empty fields?
- A. WHERE [ColumnName] = 'NULL'
- B. WHERE [ColumnName] IS NOT NULL
- C. WHERE [ColumnName] = NULL
- D. WHERE [ColumnName] IS NULL
Answer: D
Explanation:
This question falls under theData Analysisdomain, focusing on SQL queries to identify data issues. The task is to find records with empty fields, which in SQL means NULL values.
* WHERE [ColumnName] = NULL (Option A): In SQL, NULL cannot be compared using "="; this syntax is incorrect.
* WHERE [ColumnName] IS NULL (Option B): This is the correct SQL syntax to identify NULL values, which represent empty fields.
* WHERE [ColumnName] IS NOT NULL (Option C): This finds non-empty fields, the opposite of the requirement.
* WHERE [ColumnName] = 'NULL' (Option D): This checks for the string "NULL," not a true NULL value, which is incorrect.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," such as identifying NULL values with IS NULL.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 48
A data analyst receives four files that need to be unified into a single spreadsheet for further analysis. All of the files have the same structure, number of columns, and field names, but each file contains different values.
Which of the following methods will help the analyst convert the files into a single spreadsheet?
- A. Appending
- B. Merging
- C. Clustering
- D. Parsing
Answer: A
Explanation:
This question is part of theData Acquisition and Preparationdomain, which involves combining data from multiple sources. The files have the same structure but different values, meaning theyneed to be stacked vertically into one dataset.
* Merging (Option A): Merging typically involves joining datasets on a common key (e.g., a customer ID), which isn't indicated here since the files only differ in values, not keys.
* Appending (Option B): Appending stacks datasets vertically, combining rows from files with the same structure into a single dataset, which matches the scenario.
* Parsing (Option C): Parsing involves breaking down data (e.g., splitting text), not combining files.
* Clustering (Option D): Clustering is a machine learning technique for grouping similar data points, not for combining files.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as appending datasets with identical structures.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.
NEW QUESTION # 49
Which of the following data repositories should a company use when structured data about the whole company needs to be stored in a predefined data structure?
- A. Data warehouse
- B. Data lake
- C. Data silo
- D. Data mart
Answer: A
Explanation:
This question pertains to theData Concepts and Environmentsdomain, focusing on selecting the appropriate repository for structured data across an entire company. The requirement for a predefined structure narrows the options.
* Data mart (Option A): A data mart stores structured data for a specific business area (e.g., sales), not the whole company.
* Data warehouse (Option B): A data warehouse is designed to store structured data from across the entire company in a predefined schema, optimized for analytics and reporting.
* Data silo (Option C): A data silo is an isolated repository, often structured, but not designed for company-wide integration.
* Data lake (Option D): A data lake stores raw data (structured and unstructured) without a predefined structure, not suitable for this requirement.
The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data warehouse is ideal for company-wide structured data.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.
NEW QUESTION # 50
A project manager requests an unscheduled report that provides a list of clients. Which of the following frequencies is best for this report?
- A. Annual
- B. Daily
- C. Weekly
- D. Ad hoc
Answer: D
Explanation:
This question pertains to theVisualization and Reportingdomain, focusing on report delivery frequencies.
The report is described as unscheduled, meaning it's a one-time request.
* Annual (Option A): Annual frequency implies a scheduled report every year, not suitable for an unscheduled request.
* Daily (Option B): Daily frequency implies a scheduled report each day, not suitable.
* Weekly (Option C): Weekly frequency implies a scheduled report each week, not suitable.
* Ad hoc (Option D): Ad hoc reports are generated on-demand for one-time or unscheduledneeds, which matches the project manager's request.
The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and ad hoc is the best frequency for an unscheduled report.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 51
The human resources department wants to understand the relationship between the ages and incomes of all employees. Which of the following graphics is the most appropriate to present the analysis?
- A. Area plot
- B. Pie chart
- C. Scatter plot
- D. Bar chart
Answer: C
Explanation:
This question pertains to theVisualization and Reportingdomain, focusing on selecting the appropriate visualization to show a relationship between two continuous variables (ages and incomes).
* Scatter plot (Option A): A scatter plot displays individual data points on two axes (age vs. income), making it ideal for showing the relationship and potential correlation between two continuous variables.
* Area plot (Option B): Area plots are used for showing trends over time, not relationships between two variables.
* Bar chart (Option C): Bar charts are better for categorical data comparisons, not continuous variable relationships.
* Pie chart (Option D): Pie charts show proportions of a whole, not suitable for showing relationships between variables.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a scatter plot is best for showing the relationship between age and income.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 52
A data analyst needs to identify outliers from a given dataset. Which of the following visualizations is the best way to identify outliers?
- A. Gantt chart
- B. Box plot
- C. Scatter plot
- D. Waterfall chart
Answer: B
Explanation:
This question falls under the Visualization and Reporting domain, focusing on selecting the appropriate visualization to identify outliers in a dataset.
* Box plot (Option A): A box plot displays the distribution of data, including the median, quartiles, and outliers (data points beyond the whiskers), making it the best choice for identifying outliers.
* Scatter plot (Option B): A scatter plot shows relationships between two variables, and while outliers may be visible, it's not specifically designed for outlier detection.
* Gantt chart (Option C): Gantt charts are for project scheduling, not suitable for outlier identification.
* Waterfall chart (Option D): Waterfall charts show cumulative changes (e.g., financial contributions), not designed for outlier detection.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a box plot is the standard visualization for identifying outliers.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 53
Which of the following is the best reason for a company to use a CSV file to share data instead of an Excel file?
- A. CSV files are not vendor-specific.
- B. CSV files are easier to change in text editors.
- C. CSV files can store different types of encoding.
- D. CSV files are smaller in size.
Answer: A
Explanation:
This question pertains to theData Concepts and Environmentsdomain, focusing on file formats for data sharing. The task is to identify the best reason to choose CSV over Excel for sharing data.
* CSV files can store different types of encoding (Option A): While CSV files can use different encodings, this isn't the primary reason to choose them over Excel.
* CSV files are not vendor-specific (Option B): CSV is a plain-text format that can be opened by any software, unlike Excel files, which are tied to Microsoft Excel, making CSV more interoperable and the best reason for sharing.
* CSV files are smaller in size (Option C): CSV files are often smaller due to their simplicity, but this isn't always the primary reason for sharing.
* CSV files are easier to change in text editors (Option D): While true, this isn't the most compelling reason for sharing data across systems.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and CSV's vendor-neutral nature makes it ideal for sharing data.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts andEnvironments.
NEW QUESTION # 54
A data analyst needs to create a report that anticipates the number of calls received daily. Which of the following is the best statistical method to use?
- A. Inferential
- B. Diagnostic
- C. Descriptive
- D. Predictive
Answer: D
Explanation:
This question falls under theData Analysisdomain, focusing on statistical methods for forecasting. The task is to anticipate (predict) the number of daily calls, which involves looking into the future.
* Predictive (Option A): Predictive analytics uses historical data to forecast future outcomes (e.g., number of calls), which matches the requirement.
* Diagnostic (Option B): Diagnostic analytics identifies causes and patterns in historical data, not future predictions.
* Inferential (Option C): Inferential statistics make generalizations about a population, not specific forecasts.
* Descriptive (Option D): Descriptive analytics summarizes past data, not suitable for anticipating future values.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and predictive analytics is the best method for forecasting future call volumes.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 55
A sales analyst is using the following table to produce a report on January sales data:
Month
Sales
Year
Product
Jan
4324563
2024
Chair
Jan
23345
2024
Table
Jan
2456897
2024
Sofa
Jan
345456
2024
Desk
Jan
23345
2024
Table
Which of the following inconsistencies is present in the data set?
- A. Outliers
- B. Redundancy
- C. Missing values
- D. Duplication
Answer: D
NEW QUESTION # 56
A manager needs a report to be sent by email every Monday for the next six months. Which of the following is the best way to accomplish this task?
- A. Developing a recurring process
- B. Creating a data snapshot
- C. Building self-service access
- D. Waiting for the request each week
Answer: A
Explanation:
This question falls under theVisualization and Reportingdomain, focusing on report delivery methods. The task requires a report to be emailed every Monday for six months, indicating a scheduled, repeating process.
* Building self-service access (Option A): Self-service allows users to generate reports on-demand, but the manager wants automatic delivery.
* Creating a data snapshot (Option B): A snapshot captures data at a specific point, not suitable for recurring delivery over six months.
* Developing a recurring process (Option C): A recurring process schedules the report to be generated and emailed every Monday, meeting the requirement for automated delivery over six months.
* Waiting for the request each week (Option D): This is manual and inefficient, not suitable for a six- month schedule.
The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and a recurring process is ideal for scheduled email delivery.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 57
A data analyst receives a request for the current employee head count and runs the following SQL statement:
SELECT COUNT(EMPLOYEE_ID) FROM JOBS
The returned head count is higher than expected because employees can have multiple jobs. Which of the following should return an accurate employee head count?
- A. SELECT JOB_TYPE, COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS
- B. SELECT DISTINCT COUNT(EMPLOYEE_ID) FROM JOBS
- C. SELECT JOB_TYPE, COUNT DISTINCT(EMPLOYEE_ID) FROM JOBS
- D. SELECT COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS
Answer: D
Explanation:
This question falls under theData Analysisdomain of CompTIA Data+ DA0-002, which involves using SQL queries to analyze data and address issues like duplicates in datasets. The issue here is that the initial query counts all instances of EMPLOYEE_ID in the JOBS table, but employees can have multiple jobs, leading to an inflated head count. The goal is to count unique employees.
* SELECT JOB_TYPE, COUNT DISTINCT(EMPLOYEE_ID) FROM JOBS (Option A): This query is syntactically incorrect because COUNT DISTINCT(EMPLOYEE_ID) should use parentheses as COUNT(DISTINCT EMPLOYEE_ID). It also groups by JOB_TYPE, which is unnecessary for a total head count.
* SELECT DISTINCT COUNT(EMPLOYEE_ID) FROM JOBS (Option B): This query is incorrect because DISTINCT applies to the rows returned, not the COUNT function directly. It doesn't address the duplicate EMPLOYEE_ID issue.
* SELECT JOB_TYPE, COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS (Option C): While this query correctly uses COUNT(DISTINCT EMPLOYEE_ID) to count unique employees, grouping by JOB_TYPE breaks the count into separate groups, which isn't required for a total head count.
* SELECT COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS (Option D): This query correctly counts only unique EMPLOYEE_IDs by using the DISTINCT keyword within the COUNT function, providing an accurate total head count without grouping.
The DA0-002 Data Analysis domain emphasizes "given a scenario, applying the appropriate descriptive statistical methods using SQL queries," which includes handling duplicates with functions like COUNT (DISTINCT). Option D is the most direct and accurate method for a total unique head count.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 58
A manager requests a report that returns results based on a user's profile. Which of the following best describes this type of report?
- A. Static
- B. Dynamic
- C. Snapshot
- D. Real-time
Answer: C
NEW QUESTION # 59
A developer builds an online survey that requires all questions to have an answer. Which of the following inconsistencies does this setting prevent?
- A. Duplication
- B. Data corruption
- C. Missing values
- D. Completeness
Answer: C
Explanation:
This question pertains to theData Governancedomain, focusing on data quality and consistency in survey design. Requiring all questions to have an answer ensures a specific type of data quality.
* Missing values (Option A): Requiring answers prevents missing values (NULLs or blanks) in the survey responses, which is the primary inconsistency this setting addresses.
* Duplication (Option B): Duplication refers to repeated records, not prevented by requiring answers.
* Data corruption (Option C): Data corruption involves damaged or altered data, not related to missing answers.
* Completeness (Option D): Completeness is the concept of having all necessary data, but "missing values" is the specific inconsistency prevented here.
The DA0-002 Data Governance domain includes "data quality control concepts," and preventing missing values ensures data integrity in survey responses.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
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