DA0-002 Practice Test 30 — 15 Questions

Question 1

In a dataset of customer orders, you need to count the number of distinct customers who have placed orders. Which SQL aggregate function should you use?

Accepted Answer

COUNT(DISTINCT customer_id). The correct syntax to count unique non-null values in a column is COUNT(DISTINCT column_name). In this case, COUNT(DISTINCT customer_id) returns the number of different customers who have placed at least one order. This is the standard SQL aggregate function designed for exactly this purpose, and it ignores NULLs in the customer_id column.

Answer

DISTINCT COUNT(customer_id)

Answer

COUNT(customer_id)

Answer

COUNT(*)

Question 2

A database has a table 'Orders' with columns OrderID (PK), CustomerID, OrderDate, and a table 'OrderDetails' with OrderID (FK), ProductID, Quantity. To ensure that every OrderID in OrderDetails exists in Orders, which integrity constraint is enforced?

Accepted Answer

Referential integrity. Referential integrity ensures that a foreign key value in a child table (OrderDetails.OrderID) must match an existing primary key value in the parent table (Orders.OrderID) or be NULL. This constraint prevents orphaned records and maintains consistent relationships between tables. Since OrderDetails.OrderID is defined as a foreign key referencing Orders, the database enforces referential integrity to guarantee every OrderID in OrderDetails exists in Orders.

Answer

Entity integrity

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Domain integrity

Answer

User-defined integrity

Question 3

A data analyst is creating a data story about sales performance. Which THREE elements are essential for effective data storytelling? (Choose THREE.)

Accepted Answer

A clear narrative with a beginning, middle, and end.. Option B is correct because effective data storytelling requires a clear narrative arc — a beginning that sets up the situation, a middle that presents the analysis and insights, and an end that resolves the story — which guides the audience logically from data to meaning. Option C is correct because context and background information are essential to make the data interpretable, helping the audience understand the business situation, benchmarks, and why the findings matter. Option E is correct because a call to action directs the audience toward a specific decision or next step, which is the ultimate purpose of a data story and distinguishes storytelling from mere data reporting. Option A does not belong because raw data tables are supporting evidence, not an essential storytelling element; dumping raw data can overwhelm the audience and obscure the narrative. Option D does not belong because using multiple chart types is a stylistic choice for variety, not a requirement — a single well-chosen visualization can be more effective than several charts used merely for the sake of variety.

Answer

Raw data tables for reference.

Answer

Use of multiple chart types to show variety.

Question 4

Match each data sampling method to its description.

Accepted Answer

Simple Random Sampling: Every member of the population has an equal chance of being selected.. In matching, ensure you distinguish between methods: simple random gives equal chance; stratified divides by characteristic; cluster divides into groups and picks whole groups; systematic uses a fixed interval.

Answer

Simple Random Sampling: Population is divided into clusters, and entire clusters are randomly selected.

Answer

Stratified Sampling: Every kth member is selected from a list after a random start.

Question 5

Which THREE of the following are appropriate methods to handle outliers in a dataset?

Accepted Answer

Transforming the data using log transformation. Log transformation (A) is correct because applying a log (or similar power) transform compresses the scale of extreme values, reducing the leverage of right-skewed outliers and making the distribution more symmetric for downstream models. Removing the outlier records (B) is correct when outliers are confirmed to be data-entry errors or otherwise invalid observations, since deleting them prevents distortion of statistics such as the mean and variance. Capping the outlier values at a certain percentile (C), also called winsorizing, is correct because it clips extreme values to a chosen percentile boundary (e.g., the 1st and 99th percentiles), preserving the record while limiting the outlier's influence. Binning continuous variables (D) is not a dedicated outlier-handling method; it discretizes values into bins and merely masks extremes rather than addressing them. Imputing outliers with the mean (E) is inappropriate because the mean itself is distorted by the outliers, so replacing them with it introduces bias and does not resolve the underlying problem.

Answer

Binning continuous variables

Answer

Imputing outliers with the mean

Question 6

A company ingests customer clickstream data from its website. The data arrives continuously in JSON format and must be stored for real-time analytics. Which type of data source is being described?

Accepted Answer

Streaming data. The description matches a streaming data source because clickstream data arrives continuously in JSON format and must be stored for real-time analytics. Streaming data sources, such as Apache Kafka or Amazon Kinesis, ingest unbounded data in real time, enabling immediate processing and analytics without batch delays.

Answer

Transactional database

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Flat file

Answer

Data warehouse

Question 7

A data analyst is cleaning a dataset and finds that some records have duplicate entries based on customer ID. Which data quality dimension is most directly affected by these duplicates?

Accepted Answer

Uniqueness. Uniqueness is the data quality dimension that measures whether each real-world entity appears exactly once in a dataset. Duplicate records based on customer ID directly violate uniqueness because the same customer is represented multiple times. Timeliness, consistency, and accuracy describe other dimensions and are not the primary issue when duplicates exist.

Answer

Timeliness

Answer

Consistency

Answer

Accuracy

Question 8

A data scientist is merging retail transaction data from online and in-store sources. Which THREE steps are required to ensure data consistency?

Accepted Answer

Ensure product IDs are standardized across sources. Option A is correct because standardizing product IDs across online and in-store sources is essential for matching the same item across systems, preventing duplicate or mismatched records during the merge. Option B is correct because converting all monetary amounts to a common currency ensures that transaction values are comparable and can be aggregated without unit inconsistencies. Option D is correct because synchronizing timestamps to a single time zone aligns event times across sources, which is necessary for accurate chronological ordering and time-based joins. Option C is not required because removing transactions with missing customer IDs would discard valid sales data and is a data-quality choice, not a consistency requirement. Option E is not required because merging on store location alone would ignore online transactions and other key fields, producing an incomplete and inconsistent dataset.

Answer

Remove all transactions with missing customer ID

Answer

Merge data using only store location

Question 9

A data engineer is designing a data warehouse for a retail company. The fact table must record each sale transaction, including product ID, store ID, date, and quantity sold. The product details (name, category, price) are stored in a separate table. This design is an example of which data modeling concept?

Accepted Answer

Star schema. This design is a classic star schema, where a central fact table (sales transactions) contains foreign keys to dimension tables (product, store, date). The fact table stores quantitative measures (quantity sold) and foreign keys, while dimension tables hold descriptive attributes (product name, category, price). This separation optimizes query performance for OLAP workloads by reducing joins and enabling straightforward aggregations.

Answer

Data lake

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Normalization

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Snowflake schema

Question 10

A data analyst is preparing a data storytelling presentation for a non-technical audience. Which THREE techniques are most effective for communicating insights?

Accepted Answer

Using relevant visuals such as charts and graphs.. Option A is correct because relevant visuals such as charts and graphs translate quantitative findings into patterns a non-technical audience can grasp quickly, which is the core of effective data storytelling. Option D is correct because annotations that call out the most important finding direct audience attention to the key insight instead of forcing them to hunt for it, which sharpens the message. Option E is correct because a clear narrative with a beginning, middle, and end gives the presentation a logical flow that builds context, delivers the insight, and lands the takeaway for non-specialists. Option B does not belong because raw data tables overwhelm non-technical listeners with detail and belong in an appendix rather than the main story. Option C does not belong because complex statistical terminology creates a barrier for a non-technical audience and shifts focus from insight to jargon.

Answer

Including raw data tables for reference.

Answer

Adding complex statistical terms to demonstrate expertise.

Question 11

Which THREE of the following are NoSQL database types?

Accepted Answer

Document. Document databases, such as MongoDB, store data in flexible, JSON-like documents (BSON in MongoDB's case). This allows for nested structures and schema-less designs, making them a core NoSQL category distinct from relational models.

Answer

Hierarchical

Answer

Relational

Question 12

A data analyst is tasked with extracting data from a legacy system that outputs fixed-width text files. The analyst needs to parse these files into a structured format. Which tool or method is most appropriate for this task?

Accepted Answer

A scripting language such as Python. Python is the most appropriate choice because fixed-width text files require precise column slicing based on character positions, which Python's string slicing and libraries like `struct` or `pandas.read_fwf` handle natively. Unlike graphical ETL tools or spreadsheets, Python provides programmatic control to define exact field widths, handle edge cases like missing delimiters, and process large files efficiently without manual intervention.

Answer

A spreadsheet application

Answer

An ETL tool with a graphical interface

Answer

SQL

Question 13

A data analyst wants to segment customers based on purchasing behavior such as frequency, monetary value, and recency. Which TWO clustering evaluation methods can help determine the optimal number of clusters? (Select two.)

Accepted Answer

Silhouette score. The elbow method uses within-cluster sum of squares, and the silhouette score measures cohesion and separation. Both help choose k. Correlation coefficient is for association, not clustering. ANOVA and t-test are for hypothesis testing.

Answer

Correlation coefficient

Answer

ANOVA

Answer

t-test

Question 14

A data analyst needs to combine customer data from a MySQL transactional database with product data from a MongoDB document store to create a unified view for reporting. The analyst uses a SQL query that joins the tables after extracting data from both sources. Which database concept is being applied?

Accepted Answer

Join. (Join) because the scenario describes combining data from two different sources—MySQL and MongoDB—into a unified view using a SQL query that joins the tables after extraction. This is a classic example of a cross-source join, where data from disparate databases is merged based on a common key, which is the fundamental purpose of a JOIN operation in SQL.

Answer

View

Answer

Stored procedure

Answer

Index

Question 15

In a logistic regression model predicting customer churn (1 = churn, 0 = not churn), the coefficient for 'contract length' is -0.5. Which of the following is the correct interpretation?

Accepted Answer

For each unit increase in contract length, the log-odds of churn decrease by 0.5.. In logistic regression, the coefficient represents the change in log-odds of the outcome for a one-unit increase in the predictor, holding other variables constant. A coefficient of -0.5 means that for each unit increase in contract length, the log-odds of churn decrease by 0.5. This is the correct interpretation of the model's output.

Answer

Longer contract length increases the odds of churn.

Answer

The probability of churn decreases by 50% for each unit increase in contract length.

Answer

Contract length is not a significant predictor.