DA0-002 Practice Test 50 — 15 Questions

Question 1

A report uses red and green colors to indicate performance; a stakeholder who is colorblind complains. What is the BEST remedy?

Accepted Answer

Add patterns or labels in addition to color. Adding patterns or labels alongside color ensures that the data encoding is accessible to colorblind viewers without removing the informational value of color for those who can see it. This approach follows WCAG (Web Content Accessibility Guidelines) 2.1 Success Criterion 1.4.1, which requires that color is not the sole means of conveying information. In data visualization tools like Tableau or Power BI, this is implemented by adding shape markers or text annotations to series, preserving the original color scheme for non-colorblind users while providing redundant encoding.

Answer

Remove color entirely

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Change colors to blue and orange

Answer

Use a different chart type

Question 2

Which TWO of the following are considered internal data sources within an organization?

Accepted Answer

Employee payroll data. Employee payroll data (B) is a correct answer because it is generated and maintained internally by the organization's HR and finance systems, containing confidential compensation, tax withholding, and benefits information that never originates outside the company. Sales transaction records (D) are also correct because they are produced by the organization's own point-of-sale, e-commerce, or ERP systems and capture internal order, revenue, and customer purchase activity. By contrast, social media feeds (A), government census data (C), and third-party market research reports (E) are all external data sources, since they are created and published by outside parties such as social platforms, government statistical agencies, and independent research firms rather than by the organization itself.

Answer

Social media feeds

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Government census data

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Market research reports from third parties

Question 3

A data analyst needs to identify duplicate customer records. Which TWO methods are commonly used? (Select two.)

Accepted Answer

Fuzzy matching using Levenshtein distance. Option A (Fuzzy matching using Levenshtein distance) is correct because it measures the minimum number of single-character edits (insertions, deletions, substitutions) needed to transform one string into another, making it ideal for catching near-duplicate customer records that differ slightly due to typos or formatting variations. Option B (Sorting and comparing adjacent rows) is correct because once records are sorted by a key field such as name or email, duplicate or near-duplicate entries naturally cluster together, allowing efficient pairwise comparison of neighboring rows to flag matches. Option C is not a reliable, scalable method since random sampling cannot guarantee detection of all duplicates and is subjective. Option D does not help because hashing a primary key, which is unique by definition, will never reveal duplicates. Option E is too strict, as exact matching on all fields will miss duplicates that differ in even one attribute, such as a middle initial or apartment number.

Answer

Visual inspection of random sample

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Using a hash function on primary key

Answer

Exact match on all fields

Question 4

A company receives daily sales data in CSV format. The data includes a 'Date' column in MM/DD/YYYY format. To load this into a database that expects YYYY-MM-DD, the analyst should:

Accepted Answer

Use a data transformation tool to convert the date format during ETL. The correct approach is to use a data transformation tool to convert the date format during ETL. This ensures the data is standardized to the database's expected YYYY-MM-DD format without manual intervention, preserving data integrity and enabling automated, repeatable loads. Transformation tools can parse MM/DD/YYYY and reformat it consistently, which is a core ETL function.

Answer

Manually edit the CSV files before loading

Answer

Change the database schema to accept MM/DD/YYYY

Answer

Ignore the date column and use a default date

Question 5

A marketing team wants to analyze customer sentiment from social media posts. Which data acquisition method is most appropriate?

Accepted Answer

Web scraping from public social media APIs. Web scraping from public social media APIs is the most appropriate method for analyzing customer sentiment from social media posts because it directly collects the unstructured text data (posts, comments, tweets) that contains sentiment. Social media platforms provide APIs (e.g., Twitter API, Facebook Graph API) that allow programmatic access to public posts, enabling large-scale data acquisition. This method is real-time, scalable, and captures the authentic voice of customers, which is essential for sentiment analysis. Internal databases and surveys do not capture social media data, and physical sensors are irrelevant to text-based sentiment.

Answer

Internal database query

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Physical sensor data

Answer

Survey questionnaire

Question 6

Which TWO are common pitfalls when communicating data insights?

Accepted Answer

Using misleading scales on charts. Option B is correct because using misleading scales on charts—such as truncating the y-axis so it does not start at zero or using inconsistent intervals—distorts the visual representation of data and can lead the audience to draw false conclusions, making it a classic data-communication pitfall. Option E is correct because overloading the audience with too many visuals (chart junk or excessive dashboards) exceeds cognitive capacity, dilutes the key message, and causes the audience to miss the most important insights. By contrast, option A (explaining assumptions clearly) is a best practice that builds trust and transparency, not a pitfall. Option C (including a clear call to action) is also a best practice because it tells the audience what to do with the insight. Option D (providing context for the data) is likewise a best practice, since context such as baselines, benchmarks, and timeframes is essential for correct interpretation.

Answer

Explaining assumptions clearly

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Including a clear call to action

Answer

Providing context for the data

Question 7

A marketing team conducted a customer satisfaction survey for five different departments (Sales, Support, Billing, Shipping, Returns). The survey asked customers to rate their satisfaction on a scale of 1 (Very Dissatisfied) to 5 (Very Satisfied). The data is ordinal and the team wants to visualize the distribution of responses for each department to quickly see which department has the most 'Very Satisfied' customers and which has the most 'Very Dissatisfied'. They also want to compare the spread of responses across departments. Which chart type should they use?

Accepted Answer

Stacked bar chart with departments on x-axis and counts of each rating stacked. A stacked bar chart with departments on the x-axis and counts of each rating stacked shows the full distribution of ordinal responses per department, making it easy to compare which department has the most 'Very Satisfied' (top stack) and 'Very Dissatisfied' (bottom stack) responses. It also allows visual comparison of the spread across departments by comparing stack heights and segment proportions.

Answer

Line chart with departments on x-axis and average rating on y-axis

Answer

Box plot for each department

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Scatter plot with department as category and satisfaction score as value

Question 8

A simple linear regression model predicts sales (y) from advertising spend (x). The equation is y = 2.5x + 10, and R² = 0.81. Which interpretation is correct?

Accepted Answer

81% of the variation in sales is explained by advertising spend.. Slope indicates that each unit increase in x increases y by 2.5 units. R² of 0.81 means 81% of variance in y is explained by x.

Answer

The correlation between sales and advertising is 0.81.

Answer

When advertising is $0, sales are $2.5.

Answer

For every $1 increase in advertising, sales increase by $10 on average.

Question 9

A marketing company is building a customer segmentation model. The data team has access to two sources: a CRM database with customer demographics and purchase history, and a third-party data provider that offers social media activity scores. The CRM data is updated daily, while the third-party data is refreshed weekly on Sundays. The analyst needs to create a unified dataset for the model training scheduled for Wednesday morning. The analyst runs a SQL query to join the two tables on CustomerID, but the resulting dataset has far fewer rows than expected. Upon investigation, the analyst finds that many customers in the CRM do not have matching records in the third-party data. Additionally, some customers in the third-party data have multiple entries due to unresolved duplicates. The analyst must produce the most complete dataset possible while maintaining data quality. Which course of action should the analyst take?

Accepted Answer

First deduplicate the third-party data by keeping the most recent record per CustomerID, then perform a LEFT JOIN from CRM to the deduplicated third-party data.. It first resolves the duplicate issue in the third-party data by keeping the most recent record per CustomerID, ensuring each customer has a single, current social media score. Then, a LEFT JOIN from CRM to the deduplicated third-party data preserves all CRM customers, maximizing completeness while maintaining data quality. This approach aligns with the goal of producing the most complete dataset for model training, as the CRM is the primary source with daily updates.

Answer

Perform an INNER JOIN on CustomerID and then remove duplicates from the result.

Answer

Use only the third-party data because it provides the social media scores needed for segmentation.

Answer

Perform a LEFT JOIN from the third-party data to CRM, then aggregate duplicates by averaging scores.

Question 10

A data analyst is analyzing customer purchase amounts. The dataset contains several extreme high values due to luxury purchases. Which measure of central tendency is most robust to these outliers?

Accepted Answer

Median. The median is the middle value when data is ordered, so extreme high values (luxury purchases) shift it only slightly — it is a robust measure of central tendency. Unlike the mean, which is pulled upward by outliers because it sums all values, the median depends only on rank position. This makes it the preferred measure for skewed distributions like income or purchase amounts.

Answer

Range

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Mean

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Mode

Question 11

Which THREE of the following are valid methods for handling missing data?

Accepted Answer

Using a placeholder like 'Unknown' for categorical data. Using a placeholder like 'Unknown' for categorical missing data preserves the dataset's structure and allows analysis to proceed without introducing statistical bias. This method is particularly valid for nominal data where the missing category can be treated as a distinct value, enabling downstream operations like one-hot encoding or frequency analysis without distorting the original distribution.

Answer

Ignoring missing values and proceeding with analysis

Answer

Sorting the data to bring missing values to the top

Question 12

A company is building a data pipeline to ingest sensor data from IoT devices. The data arrives continuously in small batches and must be processed in real-time for monitoring. Which type of data source best describes this scenario?

Accepted Answer

Streaming data. B is correct because the scenario describes data arriving continuously in small batches that must be processed in real-time for monitoring. This is the defining characteristic of streaming data, which is typically ingested via technologies like Apache Kafka, Amazon Kinesis, or MQTT brokers, enabling low-latency processing and immediate alerting.

Answer

Transactional database

Answer

Web scraping

Answer

Flat file

Question 13

A dataset contains customer records with a column for 'Phone Number' that should be unique. However, the analyst finds several duplicate phone numbers. Which data quality dimension is primarily affected?

Accepted Answer

Uniqueness. Uniqueness measures whether each real-world entity appears exactly once in the dataset. Since 'Phone Number' is intended to be a unique identifier per customer, finding duplicate values directly violates that expectation, so the affected dimension is uniqueness. Completeness, accuracy, and consistency describe different properties (missing values, correctness, and uniformity across sources) and are not what duplicate keys violate.

Answer

Completeness

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Accuracy

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Consistency

Question 14

An analyst compares average sales across three different store locations using a statistical test. Which test is most appropriate?

Accepted Answer

ANOVA. ANOVA compares means across three or more groups.

Answer

t-test

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Correlation analysis

Answer

Chi-square test

Question 15

A retail company stores customer transaction data in a relational database. They want to analyze purchasing patterns over time. Which type of data structure best supports this analysis?

Accepted Answer

Relational table. A relational table is the correct choice because it organizes transaction data into structured rows and columns with defined schemas, enabling efficient SQL-based queries for time-series analysis (e.g., aggregating purchases by date, customer, or product). The relational model supports ACID transactions and joins across related tables (e.g., customers, products, transactions), which is essential for analyzing purchasing patterns over time while maintaining data integrity.

Answer

Graph database

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Document store

Answer

Key-value store