DA0-002 Practice Test 12 — 15 Questions

Question 1

A company uses a NoSQL document database to store product catalogs. Each product document includes fields like product_id, name, category, and price. The operations team frequently queries by product_id and by category. Which type of NoSQL database is being used, and what should be created to optimize queries by category?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Document database; create an index on category. The question explicitly states a document database is used, and the operations team frequently queries by category. In a document database like MongoDB, creating an index on the category field optimizes these queries by allowing the database to quickly locate documents without scanning every document in the collection. This is the standard approach for improving query performance on non-primary-key fields in document stores.

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Key-value store; create a secondary index on category

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Graph database; create a relationship between products

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Wide-column store; create a column family for category

Question 2

An analyst is conducting an A/B test to compare two website designs. The null hypothesis is that there is no difference in conversion rates. The p-value obtained is 0.03, and the significance threshold is 0.05. What should the analyst conclude?

Accepted Answer

Reject the null hypothesis; there is a significant difference.. In hypothesis testing, if the p-value (0.03) is less than the significance level (0.05), you reject the null hypothesis. This indicates that there is statistically significant evidence of a difference in conversion rates between the two designs. The correct conclusion is to reject the null hypothesis.

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Accept the alternative hypothesis that the new design is better.

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The test is inconclusive; need a larger sample size.

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Fail to reject the null hypothesis; there is no significant difference.

Question 3

A data analyst is creating a report to compare the performance of sales regions across multiple years. The report will be used by regional managers to identify trends. Which visualization approach best supports this?

Accepted Answer

Small multiples of line charts, one per region. Small multiples of line charts, one per region, allow regional managers to easily compare trends across years without visual clutter. Each region gets its own chart with consistent axes, making patterns and outliers clear. This approach supports the goal of identifying trends per region while enabling cross-region comparison.

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A single line chart with all regions overlaid

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A bar chart with years on x-axis and regions as grouped bars

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A stacked area chart with all regions

Question 4

A retail company wants to identify customer segments based on purchase history and demographics. Which technique is most appropriate for this task?

Accepted Answer

K-means clustering. K-means clustering is an unsupervised learning algorithm that partitions observations into k groups based on similarity across multiple features, making it ideal for segmenting customers by purchase history and demographics. It doesn't require labeled outcomes, which matches the exploratory nature of customer segmentation. The algorithm iteratively assigns points to the nearest centroid and recalculates centroids until convergence.

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Linear regression

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Chi-square test

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Logistic regression

Question 5

A data analyst wants to compare the revenue across five different product categories. Which chart type is best suited?

Accepted Answer

Bar chart. A bar chart is the best choice for comparing a categorical variable (five product categories) against a quantitative measure (revenue). Each category gets its own bar, making relative magnitudes easy to compare side by side. Bar charts handle a small number of discrete categories cleanly, which matches this scenario exactly.

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Scatter plot

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Pie chart

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Line chart

Question 6

A table named Orders has columns OrderID, CustomerID, OrderDate, and TotalAmount. Which column should be the primary key to uniquely identify each order?

Accepted Answer

OrderID. The OrderID column is the correct choice for the primary key because it contains unique values for each order, ensuring that each row can be uniquely identified. A primary key must be unique, non-null, and stable; OrderID satisfies all these requirements, whereas the other columns do not guarantee uniqueness or are subject to change.

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OrderDate

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TotalAmount

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CustomerID

Question 7

A data analyst notices that customer addresses in the database contain invalid ZIP codes. Which data quality dimension is being violated?

Accepted Answer

Validity. A is correct because validity refers to the degree to which data conforms to its defined format, rules, or constraints. Invalid ZIP codes (e.g., a five-digit code containing letters or a non-existent postal code) directly violate the format and domain rules expected for that field, making this a validity issue.

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Timeliness

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Consistency

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Completeness

Question 8

A data analyst wants to show the relative proportions of defects by type in a manufacturing process. There are 6 defect types. Which chart is most appropriate?

Accepted Answer

Pie chart. A pie chart is standard for showing parts of a whole, especially with few categories. Bar chart is also possible but not the best for proportions. Scatter and line charts are not for proportions.

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Line chart

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Stacked bar chart

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Scatter plot

Question 9

A data analyst is performing hypothesis testing to compare the mean sales of two store locations. Which TWO conditions must be satisfied to use a two‑sample t‑test? (Select TWO.)

Accepted Answer

The data is approximately normally distributed. The two-sample t-test assumes that the data in each group are approximately normally distributed. This is a key parametric assumption; if the sample sizes are large (typically n > 30), the Central Limit Theorem can relax this requirement, but for smaller samples, normality must hold to ensure valid test statistics and p-values.

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The data is paired between the two locations

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The sample sizes are equal

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The variances of the two populations are equal

Question 10

A data analyst is building a narrative around a quarterly sales decline. The story should follow a narrative arc. Which THREE elements should be included in the story?

Accepted Answer

Resolution: By diversifying suppliers, sales recovered in November.. The question asks for the three elements of a narrative arc, which are Situation, Complication, and Resolution. Option C is correct because it establishes the Situation, the baseline context that Q3 sales were on track to meet targets, which sets the stage for the story. Option E is correct because it introduces the Complication, the supply chain disruption that caused the October drop, creating the conflict or turning point. Option B is correct because it provides the Resolution, explaining how diversifying suppliers led to recovery in November, which closes the narrative arc. Option A is not correct because a random color scheme for every chart harms visual consistency and does not contribute to a narrative arc. Option D is not correct because a detailed description of each product's sales breakdown is granular data detail, not a structural element of the narrative arc.

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Random color scheme for every chart

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Detailed description of each product's sales breakdown

Question 11

In Tableau, a data analyst creates a calculated field to compute the average sales per customer. The analyst wants this calculation to remain constant regardless of the level of detail in the view. Which Tableau feature should be used?

Accepted Answer

Level of Detail expression. A Level of Detail (LOD) expression fixes the granularity of a calculation independently of the dimensions in the view, so the average sales per customer stays constant no matter how the view is sliced. LOD expressions like {FIXED [Customer] : AVG([Sales])} compute at the specified level and then can be reused at any view level of detail.

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Table calculation

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Parameter

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Filter

Question 12

A financial analyst wants to create a dashboard that shows the monthly profit and loss, highlighting how each component (revenue, cost of goods sold, operating expenses) contributes to the final net profit. Which chart type is most appropriate?

Accepted Answer

Waterfall chart. A waterfall chart is purpose-built to show how an initial value is incrementally increased or decreased by a series of positive and negative contributors to arrive at a final total. For a P&L, it starts at revenue, subtracts COGS and operating expenses as downward steps, and lands on net profit — making each component's contribution visually explicit. This is exactly the 'bridge' or 'walk' visualization finance teams use for variance and P&L analysis.

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Pie chart

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Stacked bar chart

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Area chart

Question 13

An analyst needs to show the part-to-whole relationship of market share among four competitors. Which chart type is most appropriate, considering best practices?

Accepted Answer

Pie chart. A pie or donut chart is suitable for part-to-whole with few categories (5-7 slices max). Here, four competitors fit well.

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Scatter plot

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Histogram

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Box plot

Question 14

Refer to the exhibit. What is the best corrective action to resolve this error?

Accepted Answer

Convert the 'revenue' column to numeric data type during ETL. The error shown in the exhibit is a type mismatch: the 'revenue' column contains string values, but the visualization expects a numeric measure for aggregation and charting. The correct fix is to convert the column to a numeric data type during the ETL process so that calculations and visual encodings work as intended. Changing the chart type or removing the column only masks the symptom rather than resolving the underlying data quality issue.

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Change the chart type to a bar chart

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Remove the 'revenue' column from the visualization

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Use a string-compatible chart type

Question 15

A data analyst needs to retrieve all unique job titles from an employees table. Which SQL keyword should be used in the SELECT clause?

Accepted Answer

DISTINCT. The DISTINCT keyword in a SELECT clause eliminates duplicate rows from the result set, returning only unique combinations of the selected columns. When applied to a single column like job_title, it returns each job title exactly once, which is precisely what the analyst needs. DISTINCT operates on the entire row of selected columns, so SELECT DISTINCT job_title FROM employees yields the unique list of titles.

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UNIQUE

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REMOVE DUPLICATES

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FILTER