DA0-002 Practice Test 3 — 10 Questions

Question 1

In Looker Studio, what is the difference between dimensions and metrics?

Accepted Answer

Dimensions are categorical; metrics are numerical. In Looker Studio, dimensions are fields that contain categorical data (e.g., text, dates, or geographic names) used to group and segment data, while metrics are numerical fields (e.g., counts, sums, averages) that can be aggregated. Option D is correct because this distinction is fundamental to how Looker Studio processes and visualizes data: dimensions define the rows or categories in a chart, and metrics provide the quantitative values to be measured.

Answer

Dimensions are used for aggregation; metrics are for grouping

Answer

Dimensions are numerical; metrics are categorical

Answer

Both can be categorical or numerical

Question 2

A data analyst at a marketing firm is creating a weekly performance report for the marketing team. The report includes metrics like click-through rates, conversion rates, and cost per acquisition. The team prefers a quick overview of the week's performance to identify trends and make decisions in their Monday morning meeting. The meeting is only 30 minutes, and the team has limited time to review data. The analyst wants to provide a report that is concise and actionable. What format should the analyst use?

Accepted Answer

A one-page executive summary with key metrics highlighted.. A one-page executive summary with key metrics highlighted provides the marketing team with a concise, actionable overview that can be quickly reviewed in a 30-minute meeting. This format aligns with the requirement for a quick overview to identify trends and make decisions without overwhelming the team with excessive detail or requiring interactive exploration.

Answer

A live dashboard with interactive filters.

Answer

A detailed spreadsheet with all data.

Answer

A 20-slide presentation.

Question 3

A data governance team is drafting a policy for handling personally identifiable information (PII). According to data governance best practices, which document should define the classification levels and handling procedures?

Accepted Answer

Data classification policy. The data classification policy is the authoritative document that defines classification levels (e.g., public, internal, confidential, restricted) and specifies handling procedures for each category, including PII. This aligns with data governance best practices, as it establishes the rules for labeling, storing, transmitting, and disposing of sensitive data. A data dictionary describes metadata and schema, not classification rules.

Answer

Data dictionary

Answer

Data quality report

Answer

Data flow diagram

Question 4

A data analyst is profiling a dataset and finds that the 'email' column contains some NULL values. Which SQL query can be used to count how many rows have a NULL email?

Accepted Answer

SELECT COUNT(*) FROM table WHERE email IS NULL. SELECT COUNT(*) FROM table WHERE email IS NULL correctly counts rows where email is NULL. COUNT(*) counts all rows in the filtered result set, and IS NULL is the only valid comparison operator for NULL in SQL. This is the standard, portable way to count NULLs.

Answer

SELECT COUNT(email) FROM table WHERE email = NULL

Answer

SELECT SUM(CASE WHEN email IS NULL THEN 1 END) FROM table

Answer

SELECT COUNT(ISNULL(email)) FROM table

Question 5

Which TWO of the following are best practices for designing an accessible data visualization? (Choose 2.)

Accepted Answer

Add text labels or patterns to differentiate elements. Option A is correct because adding text labels or patterns (e.g., hatching, shapes, or direct data labels) provides a non-color-dependent way to distinguish data series, which is essential for users with color vision deficiencies or when charts are printed in grayscale. Option E is correct because using colorblind-friendly palettes (e.g., Okabe-Ito or ColorBrewer's colorblind-safe schemes) ensures that the chosen colors remain distinguishable for people with deuteranopia, protanopia, or tritanopia, satisfying WCAG 1.4.1 (Use of Color). Option B is incorrect because relying solely on color to convey information fails accessibility guidelines and excludes users who cannot perceive certain color differences. Option C is incorrect because 3D effects distort data perception, reduce readability, and add visual clutter without improving accessibility. Option D is incorrect because animated transitions can trigger motion sensitivity issues and are not a recognized accessibility best practice for data visualization.

Answer

Rely solely on color to convey information

Answer

Use 3D effects to make charts visually appealing

Answer

Include animated transitions between views

Question 6

A data analyst is preparing a presentation for executive leadership. The analyst wants to highlight the correlation between marketing spend and revenue over the past year. Which visualization type is most appropriate for showing this relationship?

Accepted Answer

Scatter plot. A scatter plot is the most appropriate visualization for showing the relationship between two continuous variables—marketing spend and revenue—because it plots individual data points on an X-Y axis, allowing the analyst to visually assess correlation, trends, and outliers. This directly supports the goal of highlighting correlation, as the pattern of points (e.g., upward slope) indicates the strength and direction of the relationship.

Answer

Pie chart

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Bar chart

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Histogram

Question 7

Drag and drop the steps to clean a dataset with missing values in the correct order.

Accepted Answer

Step 1: Identify missing values, Step 2: Analyze missingness pattern, Step 3: Select imputation/removal strategy, Step 4: Apply the strategy, Step 5: Document changes. Cleaning starts with identification, then analysis of missingness, strategy selection, application, and documentation.

Answer

Step 1: Analyze missingness pattern, Step 2: Identify missing values, Step 3: Select imputation/removal strategy, Step 4: Apply the strategy, Step 5: Document changes

Answer

Step 1: Identify missing values, Step 2: Select imputation/removal strategy, Step 3: Analyze missingness pattern, Step 4: Apply the strategy, Step 5: Document changes

Answer

Step 1: Identify missing values, Step 2: Analyze missingness pattern, Step 3: Apply the strategy, Step 4: Document changes, Step 5: Select imputation/removal strategy

Question 8

In pandas, you have a DataFrame 'df' with columns 'product' and 'sales'. You want to calculate the total sales per product. Which method should you use?

Accepted Answer

df.groupby('product')['sales'].sum(). The groupby('product')['sales'].sum() pattern is the canonical pandas idiom for split-apply-combine aggregation: it groups rows by the 'product' column, selects the 'sales' Series, and applies the sum aggregator to each group, returning a Series indexed by product. This is the most direct and efficient way to compute total sales per product.

Answer

df['sales'].apply(sum)

Answer

df.pivot_table(values='sales', index='product', aggfunc='sum')

Answer

df.merge(df, on='product')

Question 9

A data analyst at a subscription media company is asked to determine whether a new recommendation engine increased average watch time. The analyst plans a two-sample t-test comparing a randomly assigned control group against a randomly assigned treatment group, with watch time recorded in minutes per user per week. Which TWO conditions must hold for the pooled-variance two-sample t-test to be statistically valid here? (Choose two.)

Accepted Answer

The population variances must be equal, or the sample sizes must be large enough for the test's robustness.. The pooled two-sample t-test rests on independent observations and an assumption about variance equality that is relaxed by large, balanced samples. Random assignment to control and treatment satisfies independence, and checking variance similarity or group balance safeguards the test's nominal error rate. Sample-size thresholds and measurement-scale restrictions are not assumptions of this procedure, so they do not belong in the analyst's validation checklist.

Answer

Both groups must contain at least 30,000 users to satisfy the central limit theorem.

Answer

Watch time must be measured on a ratio scale with a true zero and no upper bound.

Answer

The control and treatment groups must have identical means before the experiment begins.

Question 10

A data analyst needs to visualize sales per capita across U.S. states. States with small populations but high sales (e.g., Delaware) appear too prominent on a choropleth map. Which technique best addresses this issue?

Accepted Answer

Use a choropleth map with rates instead of raw sales. Using a choropleth map with rates (e.g., sales per capita) instead of raw sales normalizes the data by population, preventing states with small populations from appearing overly prominent. This addresses the issue where Delaware, with high sales but low population, dominates the map. Rates allow for fair comparison across states regardless of population size.

Answer

Switch to a bar chart sorted by sales

Answer

Use a bubble chart with size proportional to sales

Answer

Apply a log scale to the color gradient