DA0-002 Practice Test 37 — 15 Questions

Question 1

A retail company wants to analyze customer purchase patterns to identify products frequently bought together. Which data mining technique is most appropriate?

Accepted Answer

Association rules. Association rules are specifically designed to uncover relationships between items in transactional datasets, such as 'customers who buy X also buy Y.' This technique generates rules like {bread, butter} → {milk} with metrics such as support, confidence, and lift, directly answering the question of which products are frequently bought together. Classification, clustering, and regression serve different purposes: they predict labels, group similar instances, or model continuous relationships, respectively. Therefore, association rules are the most appropriate choice for market basket analysis.

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Classification

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Clustering

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Regression

Question 2

Refer to the exhibit. What does the query return?

Accepted Answer

Customers who have placed more than 5 orders.. The query uses a HAVING clause with COUNT(*) > 5, which filters groups (by customer ID) to only those with more than 5 orders. The GROUP BY customer ID ensures the count is per customer, so the result is customers who have placed more than 5 orders. Option C is correct because the condition is strictly greater than 5, not at least 5.

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All orders grouped by customer ID.

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Customers who have placed at least 5 orders.

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All customers who have placed orders.

Question 3

Which database concept ensures that data in one table corresponds to data in another table, preventing orphan records?

Accepted Answer

Referential integrity. Referential integrity is the database principle that ensures relationships between tables remain consistent — every foreign key value must match an existing primary key value, preventing orphan records. It is enforced through constraints like FOREIGN KEY with ON DELETE/UPDATE actions.

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Index

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Foreign key

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Primary key

Question 4

A data analyst at a retail company is building a dashboard for store managers to track sales performance. The data comes from three sources: point-of-sale (POS) systems, inventory, and customer loyalty. The POS table contains columns transaction_id, store_id, date, product_id, quantity, and price. The inventory table has product_id, store_id, stock_level, and reorder_point. The loyalty table has customer_id, transaction_id, and points_earned. The analyst creates a star schema with a sales_fact fact table containing all rows from POS, dimension tables for store, product, date, and customer. To calculate average transaction value, the analyst uses the formula SUM(quantity * price) / COUNT(*). Store managers report that the average transaction value appears too low, especially for stores with multiple registers. The analyst realizes that because each product sold in a transaction creates a separate row in sales_fact, a single transaction with multiple items contributes multiple rows. The current calculation divides by the number of rows rather than the number of distinct transactions. Which of the following is the best course of action to correct the average transaction value metric? (Choose one.)

Accepted Answer

Create a calculated field that sums sales per transaction (quantity * price) and then averages across distinct transaction IDs. The metric is wrong because the denominator counts fact rows (one per product line), not transactions. The correct approach is to compute the transaction-level total (SUM of quantity * price grouped by transaction_id) and then average those distinct transaction totals. Option D captures this exactly: sum sales per transaction, then average across distinct transaction IDs, which yields the true average transaction value.

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Use the MEDIAN function instead of AVG

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Aggregate the data at the transaction level before calculating the average

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Use a different data model that denormalizes transaction totals into a new fact table

Question 5

A dataset contains employee salaries ranging from $30,000 to $200,000. An analyst wants to scale the salaries to a range of 0 to 1 for use in a distance-based clustering algorithm. Which method should they use?

Accepted Answer

Min-max normalization. Min-max normalization rescales values linearly to a fixed range, typically [0, 1], using the formula (x − min) / (max − min). This is exactly what the analyst needs for a distance-based clustering algorithm, where features on different scales would otherwise dominate the distance metric. It preserves the relative ordering and shape of the distribution while bounding all values between 0 and 1.

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Log transformation

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Robust scaling

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Z-score standardization

Question 6

A data analyst is reviewing a dataset containing house prices. The mean price is $350,000 and the median is $280,000. Which of the following best describes the distribution of house prices?

Accepted Answer

The distribution is right-skewed.. When the mean ($350,000) is greater than the median ($280,000), the distribution is right-skewed (positively skewed). In a right-skewed distribution, a long tail of high values pulls the mean upward above the median. This is common with house prices, where a few very expensive homes inflate the mean.

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The distribution is symmetric.

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The distribution is left-skewed.

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The distribution is bimodal.

Question 7

A retail company stores customer purchase history in a relational database. The database contains a table 'transactions' with columns: transaction_id, customer_id, product_id, quantity, price, and transaction_date. A data analyst needs to create a report that shows total revenue per customer for the last quarter. Which data concept describes the relationship between customer_id and total revenue?

Accepted Answer

Derived attribute. Total revenue is calculated by summing (quantity * price) for each customer, making it a derived attribute because it is computed from existing stored data (quantity and price) rather than stored directly. In the context of the 'transactions' table, customer_id is a stored key, but total_revenue is not stored; it is derived via aggregation, which matches the definition of a derived attribute in database design.

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Foreign key

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Composite attribute

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Atomic attribute

Question 8

A marketing analyst needs to combine customer data from a CRM database with social media engagement data from a third-party API. Which data acquisition method is most appropriate?

Accepted Answer

API integration. API integration is the most appropriate method because it allows the analyst to programmatically retrieve structured social media engagement data directly from the third-party service's RESTful or GraphQL API endpoints. This approach ensures real-time or near-real-time data synchronization, supports authentication (e.g., OAuth 2.0), and returns data in standardized formats like JSON or XML, which can be directly ingested into the CRM system without manual intervention.

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Web scraping

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Manual data entry

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Batch file upload

Question 9

An analyst wants to compare the mean sales revenue across three different store regions. The data is normally distributed and variances are equal. Which statistical test is most appropriate?

Accepted Answer

ANOVA. ANOVA (Analysis of Variance) is the correct test for comparing means across three or more groups when data is normally distributed and variances are equal (homogeneity of variance). It tests the null hypothesis that all group means are equal using an F-statistic comparing between-group to within-group variance.

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Two-sample t-test

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Paired t-test

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Chi-square test

Question 10

A data scientist is performing a hypothesis test with a significance level α=0.05. The p-value obtained is 0.03. What should the scientist conclude?

Accepted Answer

Reject the null hypothesis because the p-value is less than the significance level.. The decision rule for hypothesis testing is: if p-value < α, reject the null hypothesis. Here p = 0.03 and α = 0.05, so 0.03 < 0.05, meaning the result is statistically significant and the null hypothesis should be rejected in favor of the alternative. This indicates the observed effect is unlikely to have occurred by chance alone at the 5% significance level.

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Fail to reject the null hypothesis because the p-value is greater than 0.01.

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The test is inconclusive, need a larger sample size.

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Accept the null hypothesis because the p-value is small.

Question 11

A data analyst is performing data cleaning. Which THREE steps are part of this process? (Choose three.)

Accepted Answer

Correcting inconsistent data. Data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a dataset, so option A (Correcting inconsistent data) is correct because fixing mismatched formats, units, or values (e.g., 'NY' vs 'New York') is a core cleaning task. Option C (Handling missing values) is correct because cleaning must address nulls or blanks through imputation, deletion, or flagging so downstream analysis is not skewed. Option E (Removing duplicate records) is correct because duplicate rows inflate counts and distort aggregates, and deduplication is a standard cleaning step. Option B (Normalization) is not part of cleaning; it is a data transformation/scaling technique (e.g., min-max or z-score) typically applied during preprocessing/modeling. Option D (Feature engineering) is also not cleaning; it is the creation of new derived variables for modeling, which occurs after cleaning.

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Normalization

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Feature engineering

Question 12

A data analyst needs to visualize the relationship between two continuous variables, such as sales revenue and advertising spend, to identify potential correlation. Which chart type is most appropriate?

Accepted Answer

Scatter plot. A scatter plot is the most appropriate chart to visualize the relationship between two continuous variables, such as sales revenue and advertising spend. It plots each data point on a two-dimensional plane, allowing the analyst to observe patterns, trends, and potential correlation.

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Pie chart

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Line chart

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Bar chart

Question 13

You are a data analyst at a logistics company. The operations manager wants to reduce delivery delays. You have historical data including order date, delivery date, distance, weather conditions, and driver ID. Initial analysis shows that the average delivery time has increased over the past six months. You suspect that weather is a contributing factor, but you need to confirm. The company also wants to build a model to predict delivery times to better manage customer expectations. The data contains missing values for weather conditions in about 10% of records, and some driver IDs are incorrect. You have limited time and resources. What should you do first?

Accepted Answer

Start by cleaning the data: correct driver IDs and decide how to handle missing weather data, then perform exploratory data analysis. Data cleaning and exploratory data analysis (EDA) are foundational steps before any modeling or time series work. With missing weather data (10%) and incorrect driver IDs, proceeding without cleaning would introduce bias and errors. EDA will reveal patterns, correlations, and data quality issues, enabling informed decisions on imputation and feature engineering for the predictive model.

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Immediately focus on time series analysis to look for patterns

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Collect more data to fill missing values

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Build a predictive model using all available data after imputing missing weather data

Question 14

A data analyst is creating a report on customer satisfaction scores across different regions. The analyst wants to highlight regions that are significantly below average. Which of the following statistical methods is most appropriate for identifying these outliers?

Accepted Answer

Box plot with interquartile range (IQR) to identify outliers.. A box plot with interquartile range (IQR) is the most appropriate method because it explicitly identifies outliers as data points falling below Q1 - 1.5*IQR or above Q3 + 1.5*IQR. This directly addresses the analyst's goal of highlighting regions significantly below average, as the IQR method is a standard statistical technique for detecting extreme values in a distribution.

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Bar chart with average line.

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Pie chart of satisfaction categories.

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Scatter plot of satisfaction vs. region.

Question 15

During data profiling, an analyst wants to identify the number of distinct values in a column. Which SQL function should be used?

Accepted Answer

COUNT(DISTINCT column). COUNT(DISTINCT column) returns the number of unique non-null values in a column, which is exactly what data profiling requires to measure cardinality. It is the standard SQL aggregate for distinct value counts.

Answer

DISTINCT(column)

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COUNT(*)

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COUNT(column)