DA0-002 Practice Test 41 — 15 Questions

Question 1

A data analyst is preparing a dataset for a predictive model. The dataset contains a feature 'age' with values ranging from 18 to 80, and a feature 'income' ranging from 20,000 to 200,000. To ensure both features contribute equally to distance-based algorithms, which transformation should the analyst apply?

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

Accepted Answer

Standardization (z-score). Standardization (z-score) transforms features to have a mean of 0 and a standard deviation of 1, which ensures that both 'age' (18–80) and 'income' (20,000–200,000) contribute equally to distance-based algorithms like k-NN or k-means. Unlike min-max normalization, standardization is not affected by outliers and preserves the relative distances between data points, making it the preferred choice when the data does not follow a uniform distribution.

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Min-max normalization

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Log transformation

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Box-Cox transformation

Question 2

A data governance team is establishing policies for data quality. Which THREE of the following are common dimensions of data quality? (Select 3)

Accepted Answer

Consistency. Consistency is a common dimension of data quality because it ensures that data values are uniform across different datasets or systems, preventing contradictions. For example, if a customer's address is stored as '123 Main St' in one database and '123 Main Street' in another, consistency rules would flag this discrepancy. This dimension is critical for reliable reporting and integration.

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Velocity

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Volume

Question 3

A retail analyst needs to determine the most popular product category. The dataset includes columns: ProductID, Category, SalesDate, QuantitySold, UnitPrice. Which column contains qualitative data?

Accepted Answer

Category. Qualitative data (also called categorical data) represents non-numeric categories or labels. The 'Category' column contains text values such as 'Electronics' or 'Clothing', which are descriptive and cannot be used in arithmetic operations. This makes it the only qualitative column in the dataset.

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SalesDate

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QuantitySold

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UnitPrice

Question 4

A company wants to segment its customers into distinct groups based on purchasing behavior. Which algorithm is best suited for this task?

Accepted Answer

K-means clustering. K-means clustering is an unsupervised learning algorithm that partitions data into K distinct clusters based on feature similarity, making it ideal for segmenting customers by purchasing behavior without predefined labels. It groups customers who exhibit similar purchasing patterns, enabling the company to identify natural segments for targeted marketing.

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Decision tree

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Logistic regression

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Linear regression

Question 5

Which chart type is best for comparing the distribution of a continuous variable across different categories?

Accepted Answer

Box plot. Box plots provide a summary of distribution including median, quartiles, and outliers, making them ideal for comparing distributions across groups.

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Pie chart

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Histogram

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Treemap

Question 6

A data architect is designing a schema for a product catalog where each product has a variable number of attributes. Which NoSQL database type is most appropriate?

Accepted Answer

Document store. A document store (e.g., MongoDB, Couchbase) is the most appropriate choice because it stores data in flexible, self-describing documents (typically JSON or BSON), allowing each product to have a variable number of attributes without requiring a predefined schema. This directly matches the requirement of a product catalog where attributes can differ per product, unlike rigid relational tables that would require complex EAV (Entity-Attribute-Value) patterns or frequent schema migrations.

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Graph database

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Key-value store

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Relational database

Question 7

A data analyst needs to join two tables in a SQL database: Orders and Customers. The analyst wants to include all orders, even if there is no matching customer record. Which type of join should be used?

Accepted Answer

LEFT JOIN. A LEFT JOIN returns all rows from the left table (Orders) and the matching rows from the right table (Customers). If there is no match, NULL values are returned for the right table's columns. This satisfies the requirement to include all orders, even those without a matching customer record.

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RIGHT JOIN

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FULL OUTER JOIN

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INNER JOIN

Question 8

A large financial institution is implementing a data governance framework to comply with new regulations requiring strict control over sensitive customer data. The data governance committee has identified several domains, including customer master data, transaction data, and risk assessment data. They need to decide on a master data management (MDM) approach that ensures a single, authoritative source of customer information across all systems. However, the current environment has multiple legacy systems with conflicting customer records. The committee is concerned about downtime and business disruption during the transition. Which MDM approach best balances data consistency with minimal operational impact?

Accepted Answer

Registry MDM, which creates a virtual view by linking records without moving data.. Registry MDM is correct because it creates a virtual, unified view of customer data by linking records from multiple legacy systems via a registry index, without physically moving or consolidating data. This approach ensures a single authoritative source of customer information while avoiding downtime and business disruption, as the underlying systems remain unchanged and operational during the transition.

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Centralized MDM, which replaces all systems with one central master system.

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Consolidation MDM, which merges all customer data into a single physical hub.

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Coexistence MDM, which maintains multiple masters with synchronization.

Question 9

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 10

A data analyst wants to show the sales trend for a product over the past 12 months. Which chart type is most appropriate?

Accepted Answer

Line chart. A line chart is the most appropriate choice because it explicitly shows data points over a continuous time interval, making it ideal for visualizing the sales trend over 12 months. The x-axis represents time (months), and the y-axis represents sales values, allowing the audience to easily see upward, downward, or cyclical patterns. This aligns with the core principle of time-series visualization, where line charts excel at highlighting trends and changes over sequential periods.

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Bar chart

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Pie chart

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Histogram

Question 11

A data engineer needs to acquire data from a legacy mainframe system that does not support modern APIs or direct database connectivity. Which approach is most feasible?

Accepted Answer

Use FTP to transfer flat files. FTP (File Transfer Protocol, RFC 959) is a widely supported, low-overhead method for transferring flat files (e.g., CSV, EBCDIC-encoded text) from legacy mainframe systems that lack modern APIs or direct database connectivity. Mainframes like IBM z/OS natively support FTP, allowing the data engineer to schedule periodic file exports without requiring system modernization or complex middleware.

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Re-platform the mainframe to a modern system

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Use a database gateway

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Manual data entry

Question 12

An analyst needs to display the number of website visitors per age group (e.g., 18-25, 26-35, 36-45). Which chart type is most appropriate?

Accepted Answer

Histogram. A histogram displays the distribution of a continuous variable (age) across bins (age groups).

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Bar chart

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Line chart

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Pie chart

Question 13

A data analyst is presenting a complex statistical analysis to a group of data scientists. The audience is highly knowledgeable. Which TWO approaches are most appropriate? (Choose two.)

Accepted Answer

Include technical details and methodology. Data scientists expect rigorous technical depth; including methodology and technical details aligns with their expertise and allows them to evaluate the analysis's validity. In a highly knowledgeable audience, omitting such details would undermine credibility and hinder peer review.

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Avoid mentioning uncertainty to maintain confidence

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Use basic visualizations like pie charts

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Simplify the findings to avoid confusion

Question 14

In Tableau, which of the following is used to create a calculated field that aggregates data at a different level of detail than the view?

Accepted Answer

LOD expressions. LOD (Level of Detail) expressions allow you to compute aggregates at a granularity different from the view.

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Marks

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Parameters

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Dashboard actions

Question 15

A data analyst at a retail company is building a multiple linear regression model to forecast weekly sales. The dataset contains 50 predictor variables, including store size, promotional spend, holiday indicators, and many others. After training the model, the analyst observes an R-squared of 0.99 on the training set but only 0.55 on the holdout test set. Which action should the analyst take first to address this discrepancy?

Accepted Answer

Remove highly correlated predictor variables and apply regularization (e.g., Ridge or Lasso).. The high R-squared of 0.99 on training data versus 0.55 on test data is a classic sign of overfitting, where the model has learned noise and specific patterns in the training set that do not generalize. Removing highly correlated predictors reduces multicollinearity and model complexity, while regularization (Ridge or Lasso) penalizes large coefficients, shrinking them to prevent overfitting. This is the most direct first step to improve generalization.

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Add more predictor variables to increase the training R-squared further.

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Use k-fold cross-validation with a different random seed to get a more reliable test set estimate.

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Increase the number of hidden layers in the model to capture more complexity.