DA0-001 Practice Test 28 — 15 Questions

Question 1

A data analyst is building a linear regression model to predict sales based on advertising spend. The analyst notices that the residuals are not normally distributed and have a non‑constant variance. Which of the following transformations is most appropriate to apply to the dependent variable?

Accepted Answer

Logarithmic transformation. The logarithmic transformation is the most appropriate choice because it stabilizes non‑constant variance (heteroscedasticity) and helps make the residuals more normally distributed, which are key assumptions for linear regression. By compressing the scale of the dependent variable (sales), it reduces the impact of large values and often linearizes multiplicative relationships, such as diminishing returns from advertising spend.

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Standardization (z-score)

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Normalization (min-max scaling)

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Square root transformation

Question 2

After training a decision tree, the tree has depth 20 and 100% accuracy on training data but only 60% on test data. Which hyperparameter adjustment is most likely to improve generalization?

Accepted Answer

Increase minimum samples per leaf. The model is overfitting: 100% training accuracy vs. 60% test accuracy with a depth-20 tree. Increasing minimum samples per leaf forces the tree to be simpler by requiring more samples in each leaf, reducing variance and improving generalization. This directly combats the overfitting caused by the overly deep tree.

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Increase number of estimators

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Decrease minimum samples per split

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Increase maximum depth

Question 3

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. Option C is correct because 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 4

Which TWO of the following are dimensional modeling techniques commonly used in data warehouses?

Accepted Answer

Snowflake schema. The snowflake schema is a dimensional modeling technique where dimension tables are normalized into multiple related tables, reducing data redundancy. This structure is commonly used in data warehouses to improve query performance and maintainability for complex analytical queries.

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Entity-relationship diagram

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Scatter plot

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Histogram

Question 5

A data analyst runs the Python code shown. What is the result of executing this code?

Accepted Answer

It reads the data, adds a calculated column, and shows the first 5 rows. The code reads a CSV file into a pandas DataFrame, then creates a new column 'total' by summing columns 'col1' and 'col2'. Finally, `head()` returns the first 5 rows. Option A correctly describes this sequence of operations.

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It throws an error because 'total' column already exists

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It reads the data and displays all rows

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It reads the data and displays summary statistics

Question 6

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?

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 7

Refer to the exhibit. Which data quality dimension is being violated?

Accepted Answer

Consistency. The exhibit shows the same customer ID (C001) associated with two different customer names ('John Smith' and 'Jon Smith'), which violates the consistency dimension. Consistency requires that data values be free from contradiction and adhere to the same representation rules across the dataset. Here, the conflicting names for the same identifier break referential integrity and data uniformity.

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Uniqueness

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Timeliness

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Completeness

Question 8

Refer to the exhibit. A data analyst wants to grant read access to an entire S3 bucket named 'data-lake'. Which of the following best describes what this policy does?

Accepted Answer

Allows read access to all objects in the data-lake bucket. This policy grants read access to all objects within the 'data-lake' S3 bucket. In AWS S3, a bucket-level policy that allows the 's3:GetObject' action without a condition restricting the resource to a specific prefix or folder effectively permits reading every object in the bucket. Option D correctly identifies this behavior.

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Allows both read and write access to the bucket

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Allows only specific users to read objects

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Allows read access to a specific folder within the bucket

Question 9

In a dataset with variables on different scales (e.g., age in years and income in dollars), which preprocessing step is necessary before applying k-means clustering?

Accepted Answer

Normalization (scaling). K-means clustering relies on Euclidean distance to measure similarity between data points. When variables like age (in years) and income (in dollars) are on different scales, the variable with larger numeric values (income) will dominate the distance calculation, skewing the clustering results. Normalization (scaling), such as min-max scaling or z-score standardization, rescales all features to a comparable range (e.g., [0,1] or mean=0, variance=1), ensuring each feature contributes equally to the distance computation.

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Feature selection

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Dimensionality reduction

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One-hot encoding

Question 10

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 11

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 12

A data analyst is building a supervised learning model to predict customer churn. The target variable is binary (churn = yes/no). Which TWO modeling techniques are appropriate for this task? (Select two.)

Accepted Answer

Logistic regression. Logistic regression is appropriate because it models the probability of a binary outcome (churn yes/no) using a logistic function, making it a standard choice for binary classification tasks. It outputs a value between 0 and 1, which can be thresholded to predict the class label.

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K-means clustering

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Linear regression

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Apriori algorithm

Question 13

A retail company wants to predict sales based on advertising spend and season. Which data modeling technique should the analyst use?

Accepted Answer

Multiple linear regression. Multiple linear regression is the correct technique because the analyst needs to model a continuous outcome (sales) based on two or more predictor variables: advertising spend (continuous) and season (categorical, typically encoded as dummy variables). This allows the model to capture the independent effect of each predictor on sales, which simple linear regression cannot do because it only handles one predictor.

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Simple linear regression

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Logistic regression

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K-means clustering

Question 14

Refer to the exhibit. Before running the code, the original salary column had 50 missing values. The median was calculated as 52000. After imputation, which of the following statements is true?

Accepted Answer

The median remains unchanged. Imputing missing values with the median (52000) replaces only the 50 missing entries with that value, leaving all original non-missing values unchanged. Since the median is a positional statistic, adding values equal to the current median does not shift the middle position of the sorted data, so the median remains unchanged. This is why option C is correct.

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The mean decreased significantly

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The standard deviation increased

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The minimum value decreased

Question 15

After a marketing campaign, sales increased by 15%. The analyst wants to understand which customer segment contributed most to the increase. Which type of analysis is this?

Accepted Answer

Diagnostic analysis. Diagnostic analysis is used to understand the root cause of an event or change. In this scenario, the analyst already knows sales increased by 15% and wants to determine which customer segment drove that increase, which is a classic diagnostic question. This type of analysis goes beyond describing what happened to explain why it happened.

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Predictive analysis

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Prescriptive analysis

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Descriptive analysis