DA0-002 Practice Test 3 — 30 Questions

Question 1

In Looker Studio, you have a data source with daily sales and a separate data source with marketing spend. You want to create a chart that shows sales and marketing spend on the same axis, but the two sources are not joined natively. Which feature should you use to combine them?

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

Accepted Answer

Data blending. Data blending in Looker Studio lets you combine metrics from two different data sources in a single chart by defining a join key (dimension) shared between them. This is the native mechanism for merging unjoined sources without modifying the underlying data. It is designed exactly for scenarios like combining sales and marketing spend on the same axis.

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Filters

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Community connectors

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Calculated fields

Question 2

Which chart type is best for showing the distribution of a continuous variable, such as customer ages?

Accepted Answer

Histogram. A histogram is the correct choice because it groups continuous data (like customer ages) into bins along a continuous x-axis, displaying the frequency distribution through bar heights. This directly shows the shape, spread, and central tendency of the variable, which is the core requirement for visualizing a continuous distribution.

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Bar chart

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Pie chart

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Box plot

Question 3

A data analyst is tasked with collecting data from multiple spreadsheets provided by different departments. Each spreadsheet has different column names and formats. What is the best first step?

Accepted Answer

Develop a data dictionary and standardize column names. Developing a data dictionary and standardizing column names ensures consistency across all data sources before loading, reducing errors and facilitating integration. Immediately loading data can cause inconsistencies. Discarding mismatched data loses potentially valuable information. Using a machine learning model is an unnecessary and complex first step.

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Discard any mismatched data

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Use a machine learning model to clean data

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Immediately load all data into a database

Question 4

A data analyst is tasked with combining customer data from a CRM system and a billing system. The CRM uses a GUID for customer ID, while billing uses an integer. Which approach should the analyst use to ensure a reliable merge?

Accepted Answer

Standardize the customer ID format and use it as the join key.. Standardizing the customer ID format (e.g., converting the billing integer to a GUID or mapping both to a common string key) ensures a consistent join key across heterogeneous systems. This eliminates type mismatch errors and guarantees that each customer record can be matched reliably, as GUIDs are globally unique and integers are typically sequential, so direct comparison would fail without transformation.

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Use the customer name as the join key.

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Merge using a cross-join and then filter manually.

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Perform a fuzzy match on the customer address.

Question 5

A data analyst uses a CTE to find employees who earn more than the average salary in their department. Which SQL clause is used to define the CTE?

Accepted Answer

WITH. The WITH clause is the standard SQL syntax that introduces a Common Table Expression (CTE), allowing the analyst to define a named temporary result set (e.g., WITH dept_avg AS (SELECT dept, AVG(salary) ...)) that can then be referenced in the main query. This is defined in the SQL standard and supported by PostgreSQL, SQL Server, Oracle, and MySQL 8+. The CTE exists only for the duration of the single statement that follows it.

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DECLARE

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DEFINE

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CTE

Question 6

A data analyst at a retail company is profiling a newly acquired customer table. They observe that the 'last_purchase_date' column contains values such as '2023-13-45', '0000-00-00', and '2023-02-30'. Which data quality dimension is primarily violated?

Accepted Answer

Validity. The date values shown are not valid calendar dates, meaning they fail format and range checks. Validity ensures data adheres to defined rules, such as a date being a real date. Completeness, consistency, and uniqueness address different aspects and do not capture the core problem of impossible dates.

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Uniqueness

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Completeness

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Consistency

Question 7

In time series decomposition, a data analyst separates a retail sales series into trend, seasonal, and residual components. After decomposition, the residual component shows no pattern and is random. Which of the following best describes the seasonal component?

Accepted Answer

Regular patterns that repeat at fixed intervals.. The seasonal component captures regular, repeating patterns at fixed intervals — for example, higher retail sales every December or every weekend. Because the residual shows no pattern (random noise), the decomposition has successfully isolated the systematic periodic structure into the seasonal component. This is the defining characteristic of seasonality in time series decomposition.

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Cyclical variations lasting more than a year.

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Irregular fluctuations that cannot be predicted.

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A long-term increase or decrease in sales.

Question 8

A data analyst needs to compare sales data from the company's internal CRM with public demographic data from a government census. Which data concept best describes this scenario?

Accepted Answer

Internal vs. External data. The scenario involves comparing internal CRM data (generated and owned by the company) with external government census data (publicly sourced from outside the organization). This directly maps to the Internal vs. External data concept, where internal data is collected within the enterprise (e.g., sales transactions, customer records) and external data is acquired from third-party sources (e.g., census bureaus, market research firms). The key distinction is the data's origin and ownership, not its structure, collection method, or measurement type.

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Primary vs. Secondary data

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Structured vs. Unstructured data

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Quantitative vs. Qualitative data

Question 9

An analyst wants to show the trend of monthly sales over the past two years. Which chart type is most appropriate?

Accepted Answer

Line chart. A line chart is best for showing trends over time.

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Pie chart

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Bar chart

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Scatter plot

Question 10

A data analyst at a marketing agency is working with a dataset containing customer demographics, purchase history, and social media engagement metrics. The agency wants to perform sentiment analysis on unstructured social media comments to identify brand perception. The dataset also includes structured fields like age, income, and purchase amounts. The analyst needs to choose a storage and processing platform that can handle both structured and unstructured data efficiently without requiring extensive schema definition upfront. Which platform should the analyst recommend?

Accepted Answer

Data lake. A data lake is the correct choice because it can store both structured data (e.g., age, income, purchase amounts) and unstructured data (e.g., social media comments) in its native format without requiring a predefined schema. This flexibility allows the analyst to ingest raw social media text for sentiment analysis and later apply schema-on-read for structured queries, avoiding the upfront schema definition needed by other platforms.

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Relational database (RDBMS)

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Data warehouse

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NoSQL document database

Question 11

In Power BI, a developer needs to create a measure that calculates total sales for the same period last year. Which DAX function should be used?

Accepted Answer

SAMEPERIODLASTYEAR. SAMEPERIODLASTYEAR is a DAX time intelligence function that returns a table of dates shifted back exactly one year, ideal for year-over-year comparisons. It is specifically designed for calculating metrics like total sales for the same period last year when used with a date table. This function is simple and directly addresses the requirement.

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TOTALYTD

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PARALLELPERIOD

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DATEADD

Question 12

A database administrator wants to ensure that every value in a column matches values in a primary key column of another table. Which constraint enforces this rule?

Accepted Answer

Foreign key. A foreign key constraint enforces referential integrity by requiring that values in a column match values in the primary key (or unique key) column of another table. This is exactly the rule described — ensuring every value in one table's column corresponds to a primary key value in another table.

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Unique constraint

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Primary key

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Check constraint

Question 13

A company’s marketing team wants to segment customers based on purchase history, demographics, and website behavior. The data includes both numeric and categorical variables. Which clustering algorithm is best suited for handling mixed data types?

Accepted Answer

Hierarchical clustering with Gower distance. Hierarchical clustering with Gower distance is best suited for mixed data types because Gower distance computes a dissimilarity measure that handles both numeric and categorical variables by normalizing numeric differences and using a simple matching coefficient for categorical ones. This allows the algorithm to create a distance matrix that equally weights all variable types, making it ideal for segmenting customers with purchase history, demographics, and website behavior data.

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K-modes clustering

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DBSCAN with Euclidean distance

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K-means clustering

Question 14

An analyst computed the mean, median, and mode of a dataset and found they are all equal. Which of the following best describes the distribution?

Accepted Answer

Symmetric. When mean, median, and mode are equal, the distribution is symmetric and unimodal, often resembling a normal distribution.

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Bimodal

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Negatively skewed

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Positively skewed

Question 15

A data analyst is working with a dataset containing house prices. After building a multiple linear regression model, the analyst observes that the model performs well on training data but poorly on validation data. Which technique is most appropriate to address this issue?

Accepted Answer

Apply L2 regularization (Ridge). The model is overfitting the training data, as evidenced by high performance on training data but poor performance on validation data. L2 regularization (Ridge) adds a penalty term proportional to the square of the coefficients, which shrinks them and reduces model complexity, thereby improving generalization to unseen data.

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Decrease the training data size

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Use a polynomial transformation

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Increase the number of features

Question 16

A data team must implement a data retention policy to reduce storage costs while meeting legal requirements. Which TWO actions best achieve this?

Accepted Answer

Set data retention limits with automated deletion. Option A is correct because setting data retention limits with automated deletion enforces the legal retention policy by removing data once its required retention period expires, directly reducing stored volume and storage costs. Option E is correct because archiving historical data to tape or cloud archive tiers moves infrequently accessed data to much cheaper storage media while still preserving it for legal and compliance requirements. Together, these two actions address both cost reduction and legal retention. Option B (data compression) reduces the size of stored data but does not enforce retention or remove data that has exceeded its legal retention period. Option C (increasing primary storage capacity) raises costs rather than reducing them and does nothing to meet retention requirements. Option D (data deduplication) eliminates redundant copies but does not implement a retention policy or remove expired data.

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Use data compression

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Increase primary storage capacity

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Implement data deduplication

Question 17

A university database stores student information in a normalized schema. The 'students' table has a primary key 'student_id'. The 'enrollments' table has a foreign key 'student_id' referencing 'students'. Which two of the following are true about primary and foreign keys? (Select TWO)

Accepted Answer

A foreign key ensures referential integrity between tables. A foreign key enforces referential integrity by ensuring that every value in the foreign key column of the 'enrollments' table matches a valid primary key value in the 'students' table. This prevents orphaned records and maintains consistency across related tables in a normalized relational database.

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A foreign key must have the same name as the primary key it references

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A foreign key can reference a column that is not a primary key

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A table can have multiple primary keys

Question 18

A data analyst calculates the mean, median, and mode of a dataset. Which of the following best describes how these measures are used in descriptive statistics?

Accepted Answer

To describe the central tendency of the data. Mean, median, and mode are measures of central tendency, which are used in descriptive statistics to summarize the center point of a dataset. They describe where the data tends to cluster. The correct answer is C because these measures describe the central tendency.

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To identify outliers using standard deviation

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To test hypotheses about population parameters

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To determine the probability of an event

Question 19

A data analyst wants to show the proportion of total sales for each product category. There are 4 categories. Which chart type is most appropriate?

Accepted Answer

Pie chart. A pie chart is designed to show parts of a whole, making it the most appropriate choice for displaying the proportion of total sales contributed by each of four product categories. With only four categories, the slices remain readable and the proportional relationship is immediately clear. Pie charts work best when the number of categories is small (typically 5 or fewer) and the values sum to a meaningful total.

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Line chart

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Scatter plot

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Histogram

Question 20

An analyst creates a histogram of customer transaction amounts but observes that the distribution looks bimodal. Upon review, the analyst realizes that two different customer segments (retail and wholesale) were combined. Which action best addresses this?

Accepted Answer

Create two separate histograms, one for each segment. Creating two separate histograms, one for each customer segment (retail and wholesale), best addresses the bimodal distribution because it allows the analyst to see the underlying distributions of each segment clearly. Combining them into one histogram obscures the distinct patterns, while separating them reveals the true characteristics of each group. This is a fundamental principle of data visualization: when data contains subgroups, disaggregating can provide more meaningful insights.

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Use a single histogram with two colors for segments

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Use a box plot instead of a histogram

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Increase the number of bins to see more detail