DA0-001 Practice Test 17 — 15 Questions

Question 1

A healthcare organization is subject to strict data privacy regulations requiring the classification of all data assets. The data governance team has identified three data sensitivity levels: Public, Internal, and Restricted. They have a new data pipeline importing patient health records from multiple clinics. The records include patient names, diagnoses, treatment codes, and insurance information. The team must ensure that the classification is applied correctly and that restricted data (e.g., diagnoses) is not exposed to unauthorized personnel. However, the pipeline uses automated tagging based on metadata rules, and some fields are misclassified. What is the most effective immediate action to improve classification accuracy?

Accepted Answer

Implement data loss prevention (DLP) tools that inspect content and enforce classification rules.. Option A is correct. Implementing data loss prevention (DLP) tools with content inspection can automatically detect sensitive patterns (e.g., diagnosis codes) and apply correct classification, even if metadata rules are incomplete. Option B (manual review) is too slow for a pipeline. Option C (expanding metadata rules) can help but may still miss cases; DLP provides an additional check. Option D (encrypting all data) is a security measure but does not fix classification. DLP directly addresses misclassification by analyzing content.

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Encrypt all data at rest and in transit regardless of classification.

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Require manual review and reclassification of all incoming records.

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Expand the metadata rule set to include more keywords and patterns.

Question 2

A data audit reveals that some numbers in the "Revenue" column were manually entered from PDF invoices. This introduces potential errors. Which data concept is being addressed?

Accepted Answer

Data lineage. The scenario describes a data audit that identifies potential errors introduced by manual data entry from PDF invoices. This directly concerns the accuracy, consistency, and reliability of the data, which are core dimensions of data quality. Data quality encompasses the fitness of data for its intended use, including aspects like accuracy, completeness, and timeliness, making it the correct concept being addressed.

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Data quality

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Data security

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Data governance

Question 3

Refer to the exhibit. A data analyst runs this query to identify high-value customers. However, the result does not include customers with exactly 5 orders. Which data concept does the HAVING clause illustrate?

Accepted Answer

Data aggregation with filtering on aggregated values. HAVING filters groups after aggregation, unlike WHERE which filters rows before aggregation. This demonstrates data aggregation with filtering on aggregated values.

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Data sorting with ORDER BY

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Data joining with INNER JOIN

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Data filtering on row-level conditions

Question 4

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 5

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

Accepted Answer

Customer satisfaction rating (1-5). Customer satisfaction rating (1-5) is quantitative because it represents numerical values that can be measured and subjected to mathematical operations, such as calculating an average. Temperature in Celsius is also quantitative as it is a continuous numerical measurement on an interval scale, allowing for meaningful arithmetic comparisons. Both options involve numeric data that can be analyzed statistically.

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Product color

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Zip code

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Employee ID

Question 6

Refer to the exhibit. An Avro schema is defined as shown. Which data design concept does this represent?

Accepted Answer

Schema-on-write. Avro requires defining the schema before writing data, imposing structure at write time (schema-on-write). Schema-on-read would apply structure when reading, and schema-less design has no predefined schema.

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Schema-on-read

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Schema-less design

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Dynamic schema

Question 7

A data analyst finds that the "Age" column contains values like "N/A", "unknown", and negative numbers. Which data quality dimension is primarily affected?

Accepted Answer

Validity. Validity refers to the degree to which data conforms to defined business rules or constraints. Invalid entries (non-numeric, negative) violate the expected data type and range.

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Accuracy

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Consistency

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Completeness

Question 8

A data engineer is comparing data warehouses and data lakes. Which statement accurately describes a data warehouse?

Accepted Answer

Optimized for complex queries on structured data. A data warehouse is optimized for complex queries on structured data because it uses a schema-on-write approach, where data is cleaned, transformed, and organized into relational tables (e.g., star or snowflake schemas) before loading. This pre-processing enables efficient execution of aggregations, joins, and reporting queries using SQL, making it ideal for business intelligence and analytics. In contrast, data lakes store raw data in native formats and rely on schema-on-read, which is less performant for structured query patterns.

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Typically stores data in object storage

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Stores raw, unprocessed data

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Uses schema-on-read

Question 9

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 10

An organization wants to assign responsibility for data quality and metadata management. Which role is primarily accountable for defining data standards and ensuring data quality across a specific domain?

Accepted Answer

Data steward. The data steward is the role primarily accountable for defining data standards and ensuring data quality within a specific domain. This aligns with the DAMA-DMBOK framework, where the data steward acts as the business-side owner of data content, establishing rules for data entry, validation, and metadata management to maintain consistency and accuracy.

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Data analyst

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Data owner

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Data custodian

Question 11

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

Question 12

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

Accepted Answer

Validity. The min of 0 and max of 150 are biologically implausible for age. A 0-year-old or 150-year-old customer likely indicates invalid data, affecting validity.

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Completeness

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Accuracy

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Consistency

Question 13

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 14

Which TWO of the following are examples of semi-structured data?

Accepted Answer

XML document. XML and JSON have tags/keys but no rigid schema, making them semi-structured. CSV is structured, relational tables are structured, plain text is unstructured.

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Relational table

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Plain text file

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CSV file

Question 15

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