DA0-002 Practice Exam 52 — 20 Questions

Question 1

An analyst recommends a pricing change based on data showing price elasticity. The recommendation includes expected revenue impact. What is this an example of?

Accepted Answer

Data-driven recommendation. Data-driven recommendations follow the structure: evidence -> insight -> recommendation -> expected impact.

Answer

Uncertainty communication

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Self-service analysis

Answer

Executive summary

Question 2

A data scientist creates a box plot of employee salaries and notices many outliers above the upper whisker. What action should be taken to best understand the salary distribution?

Accepted Answer

Investigate the outliers to determine if they are data entry errors or valid extremes. Investigating outliers is essential to understand the salary distribution. Outliers above the upper whisker may represent legitimate high salaries (e.g., executives) or data entry errors, and understanding their source guides appropriate analysis. Option A is wrong because a histogram does not inherently clarify the nature of outliers. Option B is wrong because blindly removing outliers can distort the true distribution. Option C is wrong because trimming top salaries without investigation loses valuable information and biases the analysis.

Answer

Replace the box plot with a histogram of the salaries

Answer

Remove all outliers to create a more typical box plot

Answer

Trim the top 5% of salaries and recreate the box plot

Question 3

Which TWO of the following are common methods for acquiring data from external sources?

Accepted Answer

Public APIs. Public APIs (C) are a common method for acquiring data from external sources because they provide a standardized, programmatic interface (often RESTful over HTTP/HTTPS) for requesting and receiving structured data, such as JSON or XML, from third-party services like social media platforms or weather services. Web scraping (D) is another common method that involves programmatically extracting data from web pages by parsing HTML or DOM structures, often using tools like BeautifulSoup or Selenium, when no API is available.

Answer

Data warehousing

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Manual data entry

Answer

Direct database connection to an internal server

Question 4

A data analyst is tasked with extracting data from a legacy system that outputs fixed-width text files. The analyst needs to parse these files into a structured format. Which tool or method is most appropriate for this task?

Accepted Answer

A scripting language such as Python. Python is the most appropriate choice because fixed-width text files require precise column slicing based on character positions, which Python's string slicing and libraries like `struct` or `pandas.read_fwf` handle natively. Unlike graphical ETL tools or spreadsheets, Python provides programmatic control to define exact field widths, handle edge cases like missing delimiters, and process large files efficiently without manual intervention.

Answer

A spreadsheet application

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An ETL tool with a graphical interface

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SQL

Question 5

A data analyst needs to present findings to a non-technical executive audience. Which visualization type is most appropriate to communicate a clear comparison of sales performance across multiple regions for the current quarter?

Accepted Answer

Bar chart. A bar chart is the most appropriate choice because it excels at comparing discrete categories (regions) using a common baseline, making it easy for a non-technical audience to quickly see which regions performed best or worst in the current quarter. The vertical or horizontal bars provide a clear, direct visual comparison of sales performance without requiring interpretation of trends or correlations.

Answer

Scatter plot

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Line chart

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Heatmap

Question 6

A data analyst is merging two datasets from different departments. The analyst notices that the 'CustomerID' field in the first dataset is stored as an integer, while in the second dataset it is stored as a string with leading zeros. Which TWO steps should the analyst take to ensure successful data integration?

Accepted Answer

Use a left join and treat the CustomerID as a string after conversion.. Converting the integer CustomerID to a string ensures both datasets have a compatible data type for the join. This approach preserves the leading zeros in the second dataset, which are semantically significant (e.g., '00123' vs. 123). A left join is appropriate to retain all records from the primary dataset while matching on the converted key.

Answer

Perform the merge directly without transformation since databases handle type conversions automatically.

Answer

Strip all non-numeric characters from the string CustomerID before joining.

Answer

Convert the string CustomerID to an integer by removing leading zeros.

Question 7

A data analyst is performing a hypothesis test with a significance level of 0.05. The p-value obtained is 0.03. What should the analyst conclude?

Accepted Answer

Reject the null hypothesis. Since the p-value (0.03) is less than the significance level (0.05), the result is statistically significant. This means the observed data provides sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis. The analyst should conclude that there is a statistically significant effect or difference.

Answer

Fail to reject the null hypothesis

Answer

Accept the null hypothesis

Answer

The result is practically significant

Question 8

Match each data security concept to its description.

Accepted Answer

Encryption: Process of converting data into a coded form to prevent unauthorized access.. The correct matches are: Encryption - encoding data, Authentication - verifying identity, Authorization - access control, Non-repudiation - denying actions. Common confusions arise from swapping Encryption and Authentication.

Answer

Encryption: Process of verifying identity.

Answer

Authentication: Process of converting data into code.

Question 9

Refer to the exhibit. What is the best course of action for the data analyst?

Accepted Answer

Investigate source data to understand why 'Region' is missing. Missing data in a critical field like 'Region' indicates a potential data quality issue that requires root cause analysis. The data analyst should investigate the source system to determine whether the missing values result from a collection error, a transformation bug, or a legitimate null, rather than making assumptions by deleting rows or filling defaults. This aligns with the principle of data provenance and ensures that downstream insights are based on accurate, traceable data.

Answer

Change the default value to 'NA'

Answer

Delete rows with missing 'Region'

Answer

Ignore the warning as it is handled

Question 10

An analyst creates a stacked bar chart showing quarterly sales by product category. The chart becomes hard to read because some categories have very small contributions. Which redesign is most effective?

Accepted Answer

Combine small categories into an 'Other' group. Combining small categories into an 'Other' group reduces visual clutter and improves readability by aggregating negligible contributions into a single bar segment. This technique preserves the overall trend while eliminating the noise from many tiny slices that make the stacked bar chart hard to interpret.

Answer

Change to a pie chart for each quarter

Answer

Increase the width of each bar

Answer

Switch to a 3D stacked column chart

Question 11

What is the primary purpose of the HAVING clause in the query shown?

Accepted Answer

Filter groups after aggregation. The HAVING clause is used to filter groups after the GROUP BY clause has aggregated the data. In SQL, WHERE filters individual rows before aggregation, while HAVING applies conditions to the results of aggregate functions like SUM, COUNT, or AVG. Option D is correct because the query uses HAVING to restrict which grouped results appear in the final output.

Answer

Sort the results in descending order

Answer

Join two tables

Answer

Filter rows before grouping

Question 12

During a presentation, a stakeholder questions the validity of a data insight because the sample size appears small. The analyst knows the sample is statistically significant. What is the best way to address this concern?

Accepted Answer

Explain the margin of error and confidence interval used.. It directly addresses the stakeholder's concern by explaining the statistical concepts of margin of error and confidence interval, which demonstrate that the sample size is sufficient for the desired level of precision. This approach validates the data insight's reliability without dismissing the stakeholder's valid question, aligning with best practices in communicating data insights.

Answer

Ignore the question and continue the presentation.

Answer

Ask the stakeholder to trust the analysis and move on.

Answer

Agree to collect more data before finalizing the report.

Question 13

A dashboard designer is creating a sales performance dashboard and wants to minimize non-data ink to improve clarity. Which action best follows the principle of maximizing the data-ink ratio?

Accepted Answer

Removing gridlines that do not add value to the chart. It directly follows Tufte's data-ink ratio principle by removing non-data ink (gridlines that don't aid interpretation) while preserving the core data. This maximizes the proportion of ink devoted to actual sales metrics, improving clarity without sacrificing information.

Answer

Including a detailed company logo on every chart

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Adding a background image to make the dashboard more visually appealing

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Using 3-D effects on bars to make them stand out

Question 14

Which THREE of the following are valid methods for handling missing data?

Accepted Answer

Using a placeholder like 'Unknown' for categorical data. Using a placeholder like 'Unknown' for categorical missing data preserves the dataset's structure and allows analysis to proceed without introducing statistical bias. This method is particularly valid for nominal data where the missing category can be treated as a distinct value, enabling downstream operations like one-hot encoding or frequency analysis without distorting the original distribution.

Answer

Ignoring missing values and proceeding with analysis

Answer

Sorting the data to bring missing values to the top

Question 15

An organization uses a data warehouse for analytics. The data team wants to load data from source systems into the warehouse. They choose to load raw data first and then perform transformations within the warehouse. Which approach are they using?

Accepted Answer

ELT. ELT (Extract, Load, Transform) involves extracting data, loading it into the target system (e.g., data warehouse), and then transforming it there. This is common with modern cloud warehouses like Snowflake or BigQuery that handle transformations efficiently.

Answer

Data lake

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Data mart

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ETL

Question 16

When presenting data insights, which three practices help ensure clarity? (Choose THREE.)

Accepted Answer

Use consistent color coding. Consistent color coding across charts and dashboards reduces cognitive load, allowing viewers to quickly associate colors with specific categories or metrics. This practice aligns with data visualization best practices for clarity, as it leverages pre-attentive processing to speed up interpretation without requiring the audience to re-learn the mapping for each chart.

Answer

Include all data points in the chart

Answer

Overlay multiple chart types on one axis

Question 17

A data analyst is asked to create a visualization showing the trend of monthly revenue over the last five years. Which chart type is most suitable?

Accepted Answer

Line chart.. A line chart is the most suitable choice because it explicitly shows the trend of monthly revenue over a continuous time period (five years). The x-axis represents time (months), and the y-axis represents revenue, allowing the viewer to easily identify patterns, seasonality, and overall direction. This makes it ideal for time-series data visualization.

Answer

Pie chart.

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Histogram.

Answer

Bar chart.

Question 18

Which TWO actions will improve the readability of a bar chart showing quarterly sales across five regions?

Accepted Answer

Sort bars in descending order of sales. Sorting bars by sales (B) allows immediate comparison of relative performance. Adding data labels on each bar (C) provides exact figures, enhancing readability. Option A is incorrect because overlaying a line chart on a bar chart mixes data types and can obscure the bar values. Option D (vertical gridlines for every bar) often adds visual clutter without improving readability. Option E (3D bar chart) distorts perspective and makes accurate comparison difficult.

Answer

Overlay a line chart showing cumulative sales

Answer

Add vertical gridlines for every bar

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Switch to a 3D bar chart to add visual depth

Question 19

Which TWO of the following are best practices for designing a data dashboard?

Accepted Answer

Use consistent color schemes to indicate performance levels.. Consistent color schemes (e.g., red for critical, yellow for warning, green for normal) allow users to instantly interpret performance levels without cognitive overload. This aligns with dashboard design principles that prioritize clarity and rapid pattern recognition over decorative elements.

Answer

Include animated transitions between data views.

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Use 3D effects to make charts more visually appealing.

Answer

Include as many charts as possible to provide comprehensive data.

Question 20

An analyst creates a dashboard with a large amount of decorative graphics and background images. According to CompTIA Data+, which design principle is being violated?

Accepted Answer

Data-ink ratio. The data-ink ratio, a concept from Edward Tufte, states that a visualization should maximize the proportion of ink used to represent actual data relative to total ink used. By adding excessive decorative graphics and background images, the analyst reduces this ratio, making the dashboard less efficient and potentially obscuring the data. CompTIA Data+ emphasizes minimizing non-data ink to improve clarity and insight extraction.

Answer

Visual hierarchy

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Appropriate precision

Answer

Consistent color coding