DA0-002 Data Concepts and Environments — 100 Questions

Question 1

A data analyst is working with a dataset containing customer information. The dataset includes a column 'full_name' which stores first and last names together. To perform analysis on first names separately, which data concept describes the process of splitting 'full_name' into 'first_name' and 'last_name'?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Data normalization. Data normalization is the process of organizing data to reduce redundancy and improve integrity, which includes splitting composite attributes like 'full_name' into atomic values ('first_name', 'last_name'). This aligns with the first normal form (1NF) principle in database design, where each column should contain indivisible values. The data analyst is decomposing a single field into multiple, more granular fields to enable separate analysis.

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Data deduplication

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Data summarization

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Data aggregation

Question 2

Which THREE of the following are valid data quality dimensions? (Choose THREE.)

Accepted Answer

Completeness. Completeness (C) is a valid data quality dimension because it measures whether all required records, fields, and values are present, with no missing data that would make the dataset unusable. Timeliness (D) is valid because it assesses whether data is available when needed and reflects the current state of the real-world events it describes, which is critical for decision-making. Accuracy (E) is valid because it measures the degree to which data correctly represents the real-world objects, events, or values it is intended to model, often verified against authoritative sources. Encryption (A) is not a data quality dimension but a security control for protecting confidentiality, and Redundancy (B) is a storage or availability technique for fault tolerance, not a measure of data quality itself.

Answer

Encryption

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Redundancy

Question 3

When the analyst runs the query, it fails. What is the most likely reason?

Accepted Answer

The alias 'TotalValue' cannot be used in the WHERE clause.. The alias 'TotalValue' is defined in the SELECT clause but is referenced in the WHERE clause. In SQL, column aliases cannot be used in the WHERE clause because the WHERE clause is evaluated before the SELECT clause, so the alias does not yet exist at that point in the query execution order. This causes a syntax or 'unknown column' error.

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The table name 'Products' is misspelled.

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The data types of Price and Quantity are incompatible.

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The expression 'Price * Quantity' is invalid in SQL.

Question 4

A data analyst is reviewing the error log from a nightly batch load. What is the most likely cause of the error?

Accepted Answer

A row with the same primary key was already loaded in a previous batch.. In nightly batch loads, data is typically inserted into a target table. If a row with a primary key that already exists is attempted to be inserted, the database will raise a primary key constraint violation error. This is the most likely cause of such errors because batch loads often assume unique data but duplicates can occur when previous loads have already inserted those rows.

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The data type of order_id is incorrect.

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The source and target schemas are mismatched.

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The order_id field contains null values.

Question 5

Which TWO of the following are examples of data governance best practices?

Accepted Answer

Defining data owners for each dataset. Defining data owners for each dataset is a core data governance practice that establishes accountability and responsibility for data assets. Data owners are typically senior stakeholders who ensure data is managed according to policies, including access controls and quality standards. This practice aligns with frameworks like DAMA-DMBOK, which emphasizes stewardship and ownership as foundational to governance.

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Creating indexes on frequently queried columns

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Using a data lake for storage

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Encrypting all data at rest

Question 6

Drag and drop the steps for the ETL (Extract, Transform, Load) process in the correct order.

Accepted Answer

Step 1: Extract, Step 2: Cleanse, Step 3: Transform, Step 4: Load, Step 5: Verify. ETL begins with extraction, followed by cleaning, transformation, loading, and verification.

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Step 1: Extract, Step 2: Transform, Step 3: Cleanse, Step 4: Load, Step 5: Verify

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Step 1: Extract, Step 2: Load, Step 3: Transform, Step 4: Cleanse, Step 5: Verify

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Step 1: Cleanse, Step 2: Extract, Step 3: Transform, Step 4: Load, Step 5: Verify

Question 7

Drag and drop the steps to create a data visualization dashboard in the correct order.

Accepted Answer

Plan the dashboard, then Select appropriate charts, then Design the layout, then Build the dashboard, then Test the dashboard. Dashboard creation starts with planning, then chart selection, layout design, building, and testing.

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Plan the dashboard, then Design the layout, then Select appropriate charts, then Build the dashboard, then Test the dashboard

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Plan the dashboard, then Select appropriate charts, then Build the dashboard, then Design the layout, then Test the dashboard

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Select appropriate charts, then Plan the dashboard, then Design the layout, then Build the dashboard, then Test the dashboard

Question 8

Match each data visualization type to its best use case.

Accepted Answer

Bar chart: Compare quantities across categories. Correct matches: Bar chart compares categories, Line chart shows trends over time, Pie chart shows proportions. Common confusions: swapping time trend with bar chart or proportion with category comparison.

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Bar chart: Show trends over time

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Pie chart: Compare quantities across categories

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Scatter plot: Show distribution of a single variable

Question 9

Which TWO roles are primarily responsible for defining and enforcing data governance policies within an organization?

Accepted Answer

Data steward. The data steward (D) is correct because this role is operationally responsible for defining, documenting, and enforcing data governance policies on a day-to-day basis, including data quality rules, naming standards, and compliance with governance frameworks. The data owner (E) is correct because this role holds accountability for a specific data domain, approves governance policies, and ensures that data is managed and protected according to organizational and regulatory requirements. Together, the data owner sets the policy direction and accountability while the data steward implements and enforces it. The data analyst (A) primarily interprets data and produces reports rather than defining governance policy. The data architect (B) designs data structures, models, and integration frameworks but does not own governance enforcement. The data custodian (C) handles the technical storage, backup, and security of data under the direction of the owner and steward, not the definition of governance policy.

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Data analyst

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Data architect

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Data custodian

Question 10

Refer to the exhibit. A data pipeline is failing to parse this log entry. What is the most likely cause of the error?

Accepted Answer

Unquoted string for country. The log entry uses space as a delimiter. The country field 'United States' contains a space but is not enclosed in quotes. When parsing, the space within the value is misinterpreted as a delimiter, causing the parser to split the field incorrectly. This results in a parsing failure. The unquoted string containing a space is the most direct cause of the error.

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Missing comma between fields

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Incorrect data type for age

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Extra whitespace

Question 11

Which TWO data types are considered quantitative? (Select two.)

Accepted Answer

Customer satisfaction rating (1-5). Customer satisfaction rating (1-5) (A) is quantitative because it is a numeric score on an ordinal scale that measures a magnitude and can be averaged or statistically analyzed. Temperature in Celsius (B) is quantitative because it is a continuous numeric measurement on an interval scale with meaningful units. Product color (C) is qualitative (nominal categorical) since it describes a category rather than a measurable amount. Zip code (D) is qualitative despite being numeric, because it is a nominal identifier with no arithmetic meaning. Employee ID (E) is likewise a nominal identifier, not a measurable quantity.

Answer

Product color

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Zip code

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Employee ID

Question 12

Which THREE characteristics describe unstructured data? (Select three.)

Accepted Answer

Contains text, images, audio, video. Option A is correct because unstructured data encompasses formats such as text, images, audio, and video that have no predefined data model. Option D is correct because unstructured data typically requires schema-on-read, where structure is applied only when the data is queried, rather than schema-on-write. Option E is correct because data lakes are designed to store large volumes of raw, unstructured data in its native format. Option B is incorrect because unstructured data cannot be easily processed by standard SQL without prior transformation or specialized tools. Option C is incorrect because rows and columns describe structured data stored in relational databases, not unstructured data.

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Easily processed by standard SQL

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Organised in rows and columns

Question 13

Refer to the exhibit. Based on the data profiling results, what is a likely data quality issue?

Accepted Answer

Validity. Validity refers to whether data conforms to a specific format, type, or range. In data profiling, a validity issue is identified when data does not meet defined business rules or constraints, such as an email address missing '@' or a date in the wrong format. The exhibit likely shows data that violates such rules, making Validity the correct answer.

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Completeness

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Accuracy

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Consistency

Question 14

A company is implementing a data lifecycle management policy. Which stage occurs immediately after data is created?

Accepted Answer

Storage. In the data lifecycle management (DLM) model, the stage immediately following data creation is storage. Once data is generated or ingested, it must be persisted to a storage medium (e.g., disk, SSD, cloud object store) before any other operations like analysis, archival, or deletion can occur. This ensures data durability and availability for subsequent lifecycle stages.

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Deletion

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Archival

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Analysis

Question 15

Refer to the exhibit. The data shown is an example of which data concept?

Accepted Answer

Structured data. The exhibit shows data organized into rows and columns with a fixed schema (e.g., 'Name', 'Age', 'City'), which is the defining characteristic of structured data. Structured data conforms to a predefined data model, typically stored in relational databases or spreadsheets, and can be easily queried using SQL. This tabular format with consistent data types per column is the classic example of structured data.

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Unstructured data

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Metadata

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Semi-structured data

Question 16

Refer to the exhibit. Which data concept does this exhibit best represent?

Accepted Answer

JSON schema. The exhibit shows a structured data format with key-value pairs, arrays, and nested objects enclosed in curly braces, which is the standard syntax for JSON (JavaScript Object Notation). A JSON schema defines the structure, data types, and validation rules for JSON data, making it the best fit for the exhibit. The presence of fields like "name", "age", and "address" with specific types (e.g., string, integer) confirms this is a schema definition, not a generic data representation.

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Entity relationship diagram

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Data quality rule

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Data dictionary

Question 17

Refer to the exhibit. Which conclusion can be drawn from this data quality report?

Accepted Answer

The Email_Address column has a high uniqueness rate but needs improvement in validity.. The data quality report shows that the Email_Address column has a high uniqueness rate (e.g., 100% unique values), indicating no duplicate entries, but a low validity score (e.g., many entries fail format checks like missing '@' or domain). This means the column is structurally unique but contains invalid data, so it needs improvement in validity.

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The column is fully consistent but has low completeness.

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The column has low validity and low uniqueness.

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The column requires immediate action to improve completeness.

Question 18

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 19

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. 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 20

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. '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