DA0-002 Practice Test 33 — 15 Questions

Question 1

A marketing team wants to explore the relationship between advertising spend (in dollars) and resulting revenue. Which chart type is most suitable?

Accepted Answer

Scatter plot. Scatter plots reveal correlation and distribution of two continuous variables.

Answer

Line chart

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Table

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Pie chart

Question 2

Refer to the exhibit. A stakeholder complains that the line chart exaggerates the changes in sales. What is the most likely cause?

Accepted Answer

The y-axis does not start at zero. Setting beginAtZero to false truncates the y-axis, making small changes appear larger.

Answer

There are too few data points

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The data labels are incorrect

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The chart type should be a bar chart

Question 3

Refer to the exhibit. Which data quality dimension is compromised by the missing value for Charlie's salary?

Accepted Answer

Completeness. Completeness measures whether all required data is present. Charlie's missing salary value means the record is incomplete, directly violating this dimension. In data quality frameworks, completeness is assessed by the proportion of non-null values in a field, and a null salary here fails that check.

Answer

Uniqueness

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Timeliness

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Accuracy

Question 4

A data analyst is working with a dataset that contains customer names and addresses. Some records have missing state codes. Which data quality issue is this?

Accepted Answer

Incompleteness. Incompleteness is the correct answer because missing state codes in customer address records represent a lack of required data. This is a classic example of incomplete data, where fields that should contain values are left null or blank, reducing the dataset's usability for analysis.

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Duplication

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Outliers

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Inconsistency

Question 5

A database table has columns: OrderID (primary key), ProductID, CustomerID, CustomerName, OrderDate, ProductName. All products are purchased only by the customer who placed the order. Which normal form violation exists if CustomerName depends on CustomerID?

Accepted Answer

Third normal form (3NF). The table violates Third Normal Form (3NF) because CustomerName depends on CustomerID, which is not a candidate key (the primary key is OrderID). 3NF requires that every non-key attribute be non-transitively dependent on the primary key; here, CustomerName is transitively dependent on OrderID via CustomerID. Since CustomerID is a non-key attribute (it is not part of the primary key), this transitive dependency breaks 3NF.

Answer

Boyce-Codd normal form (BCNF)

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Second normal form (2NF)

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First normal form (1NF)

Question 6

A data modeler is designing a dimensional model for a sales analytics system. The fact table contains sales transactions, and the dimension tables include product, customer, and time. To reduce data redundancy, the modeler normalizes the dimension tables into multiple related tables. Which schema is being implemented?

Accepted Answer

Snowflake schema. The snowflake schema is a dimensional model where dimension tables are normalized into multiple related tables to reduce data redundancy. In this scenario, the product, customer, and time dimensions are split into sub-dimensions (e.g., product category, customer geography, time hierarchy), which is the defining characteristic of a snowflake schema. This contrasts with a star schema where dimensions remain denormalized.

Answer

Vault schema

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Star schema

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Galaxy schema

Question 7

Which THREE of the following are characteristics of a relational database?

Accepted Answer

Enforces referential integrity through foreign keys. Relational databases enforce referential integrity through foreign keys, which ensure that relationships between tables remain consistent. A foreign key in a child table must match a primary key value in the parent table, preventing orphaned records and maintaining data integrity.

Answer

Stores data in key-value pairs

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Supports NoSQL document storage

Question 8

A data analyst is working with a dataset that includes a column 'income' with values ranging from 20,000 to 150,000. To standardize this variable for a linear regression that assumes normally distributed residuals, which method should be used?

Accepted Answer

Z-score standardization. Z-score standardization transforms data to have mean 0 and standard deviation 1, which is suitable for algorithms that assume normality (like linear regression).

Answer

Log transformation

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Min-max normalization

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Square root transformation

Question 9

Which report type is best suited for monitoring the real-time status of manufacturing equipment on the factory floor?

Accepted Answer

Operational report. Operational reports provide real-time data for monitoring ongoing operations.

Answer

Scheduled report

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Self-service report

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Analytical report

Question 10

Which TWO actions are appropriate when creating a data visualization for a diverse audience with varying levels of data literacy? (Choose two.)

Accepted Answer

Use clear and descriptive axis labels.. Clear and descriptive axis labels ensure that all viewers, regardless of their data literacy level, can understand what the axes represent. This directly supports accessibility and reduces misinterpretation, which is critical when presenting to a diverse audience.

Answer

Use only one chart type throughout the report.

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Include interactive elements like drill-downs for all charts.

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Include a legend only if there are more than three data series.

Question 11

Which TWO of the following are examples of semi-structured data?

Accepted Answer

XML document. XML and JSON have tags/keys but no rigid schema, making them semi-structured. CSV is structured, relational tables are structured, plain text is unstructured.

Answer

Relational table

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Plain text file

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CSV file

Question 12

A data analyst is using pandas to clean a DataFrame. They need to replace missing values in the 'age' column with the median age. Which method should they use?

Accepted Answer

df['age'].fillna(df['age'].median()). fillna() with median() fills NaN values with the median of the column.

Answer

df['age'].replace(np.nan, df['age'].mean())

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df['age'].dropna()

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df['age'].interpolate()

Question 13

A data analyst is troubleshooting a dashboard that displays incorrect totals for sales by region. The data source queries are correct. Which of the following is the most likely cause?

Accepted Answer

The filter context is inadvertently excluding some regions.. Filter context can inadvertently exclude certain regions, leading to incorrect totals. Options A, B, and C are less likely: A affects performance not accuracy; B would be caught by correct queries; C could cause aggregation differences but filters are a common issue.

Answer

The visualizations are using a different aggregation level.

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The data model includes duplicate records.

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The dashboard is using a live connection instead of an extract.

Question 14

A healthcare organization must ensure patient data privacy when sharing reports with external auditors. Which practice is most important?

Accepted Answer

Use pseudonymization. Pseudonymization replaces identifying information with pseudonyms, allowing data utility while protecting privacy. Aggregation reduces granularity but may still reveal identities; encryption secures transport but not the content; obtaining consent is impractical for large datasets.

Answer

Encrypt the report file

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Obtain consent from patients

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Aggregate data at low granularity

Question 15

You are a data analyst at a logistics company. You have created a dashboard to monitor delivery performance. The dashboard includes a scatter plot showing delivery time (hours) vs. distance (miles) for each delivery, with points colored by delivery region (A, B, C, D, E). Users have reported that the scatter plot is cluttered because there are over 10,000 points, making it hard to see patterns. Additionally, the color legend for the five regions uses similar shades of blue, making it difficult to distinguish which region a point belongs to. You need to improve the scatter plot to reduce overplotting and improve region differentiation. Which approach is most effective?

Accepted Answer

Set point opacity to 30% and use a qualitative color palette with distinct hues for each region. Reducing opacity (alpha blending) mitigates overplotting by making overlapping points more transparent, while switching to a qualitative color palette (e.g., distinct hues like red, green, blue) ensures each of the five regions is easily distinguishable. This directly addresses both user complaints without losing the overall distribution context.

Answer

Use a hexagonal binning plot (hexbin) with color representing region

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Create five separate scatter plots (small multiples) for each region

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Convert to a bubble chart by adding package weight as bubble size