DA0-001 Comparing and Contrasting Data Concepts Practice Test 7 — 15 Questions

Question 1

A sensor records temperature readings in Celsius and a separate sensor records wind speed in meters per second. A data scientist wants to combine these datasets for analysis. Which statement accurately compares these data types?

Accepted Answer

Temperature is interval; wind speed is ratio. Temperature measured in Celsius has an arbitrary zero point (0°C does not mean 'no heat'), so it is interval data. Wind speed in meters per second has a true zero point (0 m/s means no wind), making it ratio data. Therefore, option D correctly identifies temperature as interval and wind speed as ratio.

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Both are ratio data

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Temperature is discrete; wind speed is continuous

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Both are discrete data

Question 2

A company stores customer data in a relational database with tables for orders, products, and customers. Which type of data best describes this?

Accepted Answer

Structured data. A is correct because the data is stored in a relational database with predefined schemas (tables for orders, products, and customers), which enforces a fixed structure of rows and columns. This makes it structured data, as each field has a specific data type and relationships are defined via foreign keys, enabling efficient querying with SQL.

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Unstructured data

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Qualitative data

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Semi-structured data

Question 3

A researcher wants to study the effect of a new drug. She collects data directly from clinical trial participants. Later, she compares her findings with historical data from medical journals. Which contrast best describes her data sources?

Accepted Answer

Primary vs. Secondary. Option D is correct because the researcher is directly collecting data from clinical trial participants (primary data) and then comparing it with historical data from medical journals (secondary data). Primary data is original data collected firsthand for a specific purpose, while secondary data is pre-existing data collected by others for different purposes. This contrast directly maps to the primary vs. secondary data classification in data management.

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Internal vs. External

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Quantitative vs. Qualitative

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Structured vs. Unstructured

Question 4

A data analyst notices that a column labeled 'Income' contains values like '$50,000' and '$75,000', but also 'High' and 'Low'. What data concept issue is occurring?

Accepted Answer

Mixing quantitative and qualitative data. The 'Income' column contains both numeric values (e.g., '$50,000', '$75,000') which are quantitative data, and categorical labels ('High', 'Low') which are qualitative data. Mixing these two distinct data types in a single column violates data consistency principles and prevents proper statistical analysis or machine learning processing. This is a classic example of mixing quantitative and qualitative data.

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Mixing discrete and continuous data

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Mixing nominal and ordinal data

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Mixing structured and unstructured data

Question 5

Which of the following is an example of qualitative data?

Accepted Answer

Customer feedback comments. Customer feedback comments are qualitative data because they consist of non-numerical, descriptive text that captures opinions, sentiments, or experiences. Unlike quantitative data, which can be measured or counted, qualitative data is categorical and often requires thematic analysis to derive insights.

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Stock price

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Number of website visitors

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Product weight in grams

Question 6

A data team is building a predictive model. They have data on 'Number of employees' (whole numbers) and 'Revenue' (currency). Which statement correctly compares these data types?

Accepted Answer

Number of employees is discrete; revenue is continuous. Option A is correct because 'Number of employees' is a count of distinct entities, making it discrete data (only whole numbers), while 'Revenue' can take any value within a range (including decimals), making it continuous data. Discrete data arises from counting, whereas continuous data arises from measurement.

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Both are continuous data

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Both are ratio data

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Number of employees is qualitative; revenue is quantitative

Question 7

A dataset contains a column 'Education Level' with values: 'High School', 'Bachelor', 'Master', 'PhD'. An analyst computes the average by assigning numbers 1-4. Which data concept is being violated?

Accepted Answer

Treating ordinal data as interval. The analyst assigned numeric values (1-4) to 'Education Level' categories and computed an average. This treats the ordinal data as if it were interval data, assuming equal spacing between categories (e.g., the difference between 'High School' and 'Bachelor' is the same as between 'Master' and 'PhD'), which is not valid. Ordinal data only preserves order, not magnitude or equal intervals, so calculating a mean is inappropriate.

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Misclassifying data as structured

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Treating nominal data as ordinal

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Treating ratio data as interval

Question 8

Which TWO of the following are characteristics of structured data? (Choose TWO.)

Accepted Answer

Stored in rows and columns. Option A is correct because structured data is organized into rows and columns, typically within relational databases or spreadsheets, where each column represents a specific attribute and each row represents a record. This tabular format enables efficient querying, sorting, and indexing using languages like SQL. The rigid row-and-column structure ensures data consistency and supports ACID (Atomicity, Consistency, Isolation, Durability) properties.

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Lacks a fixed schema

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Uses tags to define elements

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Consists of free-form text

Question 9

Which TWO of the following are examples of quantitative data? (Choose TWO.)

Accepted Answer

Age in years. Option B is correct because age in years is a numerical measurement that can be counted or measured on a ratio scale, making it quantitative data. Quantitative data represents quantities that can be expressed numerically and subjected to mathematical operations, such as calculating the average age of a group.

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Product color

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Customer satisfaction rating (Poor, Fair, Good)

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Country of origin

Question 10

Which THREE of the following are properties of ratio data? (Choose THREE.)

Accepted Answer

Supports multiplication and division. Ratio data supports multiplication and division because it has a true, meaningful zero point that indicates the absence of the measured attribute. This allows ratios to be computed (e.g., one value is twice another), which is a defining property of ratio scales in measurement theory.

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Data can be categorized into groups

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Allows negative values

Question 11

The exhibit shows a JSON schema for a dataset. Which statement correctly describes the data types represented?

Accepted Answer

customer_id and age are discrete quantitative; income is continuous quantitative; education is qualitative. Option C is correct because it accurately classifies the data types: customer_id (a unique identifier) is discrete quantitative, age (countable whole numbers) is discrete quantitative, income (measurable on a continuous scale) is continuous quantitative, and education (categorical levels) is qualitative. This aligns with the JSON schema where numeric fields like age and income are represented as numbers, while education is a string and customer_id is an integer.

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All fields are qualitative

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age and income are continuous; customer_id and education are discrete

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All fields are quantitative

Question 12

A retail company has merged with another firm and now needs to create a unified customer data warehouse. The existing systems use different data classification methods: System A stores customer income as a categorical range (e.g., '$0-$50k', '$50k-$100k', '$100k+') while System B stores exact income as a decimal number. A data analyst must combine these into a single table. The goal is to perform statistical analysis that includes calculating average income, but the categorical data from System A loses precision. The analyst proposes converting System B's exact values into the same ranges as System A to ensure consistency. However, the data governance team wants to preserve as much detail as possible. Which course of action should the analyst recommend?

Accepted Answer

Impute System A's categorical data with the midpoint of each range to create a continuous numeric field, then combine with System B's exact values. Option C is correct because imputing the midpoint of each income range converts System A's categorical data into a continuous numeric field, allowing it to be combined with System B's exact decimal values. This approach preserves the granularity of System B's data while enabling statistical calculations like average income across the unified dataset, balancing the data governance team's requirement for detail with the need for consistency.

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Store both columns separately and treat them as independent attributes

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Convert System B's exact income to ranges matching System A, then combine

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Use only System B's data and discard System A because it is less precise

Question 13

A healthcare analytics team is building a dashboard to monitor patient vitals. They receive data from two sources: Source 1 provides 'heart rate' as an integer (beats per minute), and Source 2 provides 'blood pressure' as a ratio (systolic/diastolic, e.g., 120/80). The team wants to create a combined metric called 'cardiac stress index' that uses both heart rate and systolic blood pressure. However, they notice that heart rate data occasionally contains negative values due to sensor errors. The data governance policy requires that all data be valid and meaningful. Which action best addresses the data quality issue while preserving the data types?

Accepted Answer

Remove all records with negative heart rate values as they are invalid. Option D is correct because negative heart rate values are physiologically impossible and violate the data governance policy requiring valid and meaningful data. Removing these records ensures the dashboard only contains accurate, actionable data without altering the original integer data type of heart rate, preserving its numerical integrity for the 'cardiac stress index' calculation.

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Convert heart rate to absolute values (remove the negative sign)

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Keep negative values but set them to NULL to indicate missing data

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Change heart rate from integer to categorical (e.g., low, normal, high) to avoid negative issues

Question 14

A retail company analyzes customer purchase data to improve inventory management. They store daily transaction records in a relational database and monthly aggregate reports in a data warehouse. Which difference between these storage methods best explains why the warehouse is more suitable for trend analysis?

Accepted Answer

The database is optimized for write-heavy OLTP, while the warehouse is optimized for read-heavy OLAP.. Option C is correct because OLTP databases are optimized for high-frequency write operations (INSERT/UPDATE/DELETE) and ACID compliance, making them ideal for transaction processing but poor for complex analytical queries. In contrast, a data warehouse is optimized for read-heavy OLAP workloads, using columnar storage, pre-aggregated tables, and indexing strategies that enable fast aggregation and trend analysis over large historical datasets. This architectural difference directly supports the retail company's need to analyze purchase trends over time.

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The database uses a star schema while the warehouse uses a normalized schema.

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The database enforces ACID transactions, while the warehouse uses eventual consistency.

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The database stores only current data, while the warehouse stores historical data.

Question 15

A data analyst is evaluating data quality issues in a customer database. Which TWO actions are best practices for ensuring data consistency?

Accepted Answer

Standardizing date formats across all tables. Standardizing date formats across all tables (Option B) ensures that date values are stored and interpreted uniformly, eliminating inconsistencies that arise from mixed formats (e.g., MM/DD/YYYY vs. DD-MM-YY). This practice directly supports data consistency by enforcing a single representation, which is critical for accurate querying, reporting, and integration across systems.

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Allowing null values for foreign keys

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Enabling cascading updates on primary keys

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Using data profiling to identify duplicate records