DA0-002 Exam Dumps - CompTIA Data+ Questions and Answers

This question pertains to the Data Concepts and Environments domain, focusing on types of server environments. The servers are located at the company’s headquarters, indicating a specific deployment model.

Cloud (Option A): Cloud environments are hosted off-site by third-party providers, not at headquarters.

On-premises (Option B): On-premises environments are located at the company’s physical location (e.g., headquarters), which matches the scenario.

Public (Option C): Public environments are cloud-based and shared across multiple organizations, not located at headquarters.

Hybrid (Option D): Hybrid environments combine on-premises and cloud, but the scenario specifies all servers are at headquarters.

The DA0-002 Data Concepts and Environments domain includes understanding "data environments," and on-premises describes a server environment located at the company’s site.

This question pertains to the Visualization and Reporting domain, 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 unscheduled needs, 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.

This question is part of the Visualization and Reporting domain, focusing on selecting the appropriate visualization for a given dataset. The grocery store wants to view revenue by department, which requires a chart that shows proportions or comparisons across categories.

Gantt (Option A): Gantt charts are used for project scheduling, not for comparing revenue across categories.

Pie (Option B): Pie charts are ideal for showing proportions or percentages of a whole, such as revenue distribution across departments, making this the best choice.

Area (Option C): Area charts are better for showing trends over time, not static categorical comparisons.

Line (Option D): Line charts are used for trends over time, not for comparing discrete categories like departments.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization" , and a pie chart is the most appropriate for showing departmental revenue proportions.

This question pertains to the Data Acquisition and Preparation domain, which in DA0-002 includes understanding data acquisition concepts and the types of data gathered from various sources, such as web scraping. Web scraping involves extracting data from websites, and the data gathered is often semi-structured, meaning it has some organizational structure but isn’t fully relational like a database table.

JSON (Option A): JSON (JavaScript Object Notation) is a semi-structured data format commonly used in web applications. Web scraping often retrieves data in JSON format via APIs or embedded scripts, as it’s lightweight and structured with key-value pairs, making it ideal for semi-structured data.

CSV (Option B): CSV (Comma-Separated Values) is a structured format typically used for tabular data. It’s not commonly the direct output of web scraping, though scraped data might be converted to CSV later.

CSS (Option C): CSS (Cascading Style Sheets) is used for styling web pages and isn’t a data format, making it irrelevant for describing scraped data.

HTML (Option D): HTML (HyperText Markup Language) is the structure of web pages and is often the raw format scraped during web scraping. While HTML is semi-structured due to its tag-based hierarchy, it’s primarily a markup language, not a data format, and the actual data extracted is often parsed into formats like JSON.

The DA0-002 Data Acquisition and Preparation domain aligns with the DA0-001 focus on "data acquisition concepts" (web ID: 14), which includes identifying formats like JSON as semi-structured data commonly acquired through web scraping. JSON is the best fit here due to its prevalence in web data exchange.

This question falls under the Data Governance domain, focusing on identifying data quality issues. The dataset includes dates of birth and birth months, which suggests a potential inconsistency.

Redundancy (Option A): The dataset includes both dates of birth (e.g., 1990-05-15) and birth months (e.g., May), which is redundant because the birth month can be derived from the date of birth, indicating a data quality issue.

Equivalence (Option B): Equivalence isn’t a standard data quality term in this context; it might refer to data matching, which isn’t the issue here.

Parallel (Option C): Parallel isn’t a recognized data quality term; it might relate to processing, not data inconsistencies.

Duplication (Option D): Duplication refers to identical records, but the issue here is redundant fields, not duplicate rows.

The DA0-002 Data Governance domain includes "data quality control concepts," and redundancy is a key inconsistency when the same information is stored in multiple forms unnecessarily.

This question pertains to the Data Governance domain, which in DA0-002 includes ensuring data quality and freshness, especially in dashboards. The issue is that the mean response time isn’t updating, while traffic data is current, indicating a potential issue with the data refresh process for the response time metric.

Refactoring the code base (Option A): Refactoring might improve long-term performance but doesn’t directly address verifying data freshness.

Testing for network connectivity issues (Option B): Network issues could cause delays, but since traffic data is updating, connectivity is likely not the issue.

Checking the last time the calculation script ran (Option C): Mean response time is a calculated metric, likely derived from a script. Checking when the script last ran directly verifies if the data refresh process failed, making this the best approach.

Determining the number of calls with no timestamps (Option D): Missing timestamps might indicate data quality issues, but it doesn’t directly verify why the mean response time isn’t updating.

The DA0-002 Data Governance domain focuses on "data quality control concepts," including ensuring data freshness in reporting. Checking the script’s last run time aligns with this objective.

This question pertains to the Data Analysis domain, 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.

This question falls under the Data Governance domain, focusing on troubleshooting data freshness issues in dashboards. The dashboard shows stale data despite a successful refresh, and the server migration updated naming conventions, suggesting a potential mismatch.

Confirm the dashboard is pointed to the newest dataset (Option A): The server migration updated dataset naming conventions, so the dashboard might still be pointing to an old dataset name, causing stale data. Confirming the dataset connection is the first step.

Filter the data in the dashboard (Option B): Filtering might adjust the view but doesn’t address the root cause of stale data.

Escalate user permissions on the server (Option C): Permissions issues would likely prevent access, not cause stale data, especially since the dataset refreshed successfully.

Verify that the dashboard subscription is not expired (Option D): An expired subscription might prevent access, but the dashboard is accessible, just showing stale data.

The DA0-002 Data Governance domain includes "data quality control concepts," such as ensuring dashboards connect to the correct, updated datasets after changes like server migrations.

This question falls under the Data Concepts and Environments domain, 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.