DA0-001 Comparing and Contrasting Data Concepts Practice Test 5 — 15 Questions

Question 1

A company is designing a data lake to store raw sensor data from IoT devices. The data arrives as JSON objects with varying schemas. Which storage approach is most appropriate?

Accepted Answer

Store raw JSON files in a distributed file system and apply schema-on-read. Option D is correct because a data lake is designed to store raw data in its native format, and IoT sensor data with varying schemas is best handled by storing raw JSON files in a distributed file system (e.g., HDFS or Amazon S3). This approach leverages schema-on-read, where the schema is applied at query time rather than at write time, allowing flexibility for heterogeneous JSON objects without data loss or transformation overhead.

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Ingest into a relational database with a predefined schema

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Store each JSON object as a separate file in a compressed columnar format

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Convert all JSON to Avro with a fixed schema before storing

Question 2

A healthcare provider needs to integrate patient data from multiple clinics into a single data warehouse. Which process is used to extract, transform, and load the data?

Accepted Answer

ETL. ETL (Extract, Transform, Load) is the correct process because the healthcare provider must first extract data from multiple source clinics, then transform it (e.g., standardize formats, clean duplicates, apply business rules) before loading it into the target data warehouse. This ensures data quality and consistency, which is critical for clinical analytics and reporting.

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ELT

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OLAP

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OLTP

Question 3

A data analyst is working with a dataset that contains customer names and addresses. Some records have missing state codes. Which data quality issue is this?

Accepted Answer

Incompleteness. Incompleteness is the correct answer because missing state codes in customer address records represent a lack of required data. This is a classic example of incomplete data, where fields that should contain values are left null or blank, reducing the dataset's usability for analysis.

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Duplication

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Outliers

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Inconsistency

Question 4

A financial institution wants to analyze transaction networks to detect fraud rings. Which database type is best suited for this analysis?

Accepted Answer

Graph database. A graph database is designed to store and traverse relationships between entities, making it ideal for analyzing transaction networks where connections between accounts, merchants, and transactions reveal fraud rings. Its native graph model (nodes and edges) allows efficient pattern matching and pathfinding queries, such as detecting circular transactions or shared attributes, which are common in fraud detection.

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Wide-column store

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Key-value store

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Document store

Question 5

A data architect is designing a schema for a product catalog where each product has a variable number of attributes. Which NoSQL database type is most appropriate?

Accepted Answer

Document store. A document store (e.g., MongoDB, Couchbase) is the most appropriate choice because it stores data in flexible, self-describing documents (typically JSON or BSON), allowing each product to have a variable number of attributes without requiring a predefined schema. This directly matches the requirement of a product catalog where attributes can differ per product, unlike rigid relational tables that would require complex EAV (Entity-Attribute-Value) patterns or frequent schema migrations.

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Graph database

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Key-value store

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Relational database

Question 6

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 7

During an ETL process, a data quality check fails due to duplicate customer IDs. Which data quality dimension is violated?

Accepted Answer

Uniqueness. Duplicate customer IDs violate the uniqueness dimension because uniqueness ensures that each record in a dataset has a distinct identifier with no duplicates. In an ETL process, a primary key or unique constraint on the customer ID column would reject duplicate values, causing the data quality check to fail. This is distinct from consistency, which checks for logical agreement across data sources.

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Consistency

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Completeness

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Accuracy

Question 8

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 9

Which THREE data quality dimensions are commonly assessed in a data profiling task?

Accepted Answer

Consistency. Consistency is a core data quality dimension assessed in data profiling because it evaluates whether data values are free from contradiction and adhere to the same representation rules across records. In profiling tools like Informatica or Talend, consistency checks identify violations such as 'NY' vs 'New York' in a state column, ensuring semantic uniformity.

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Scalability

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Availability

Question 10

Which TWO of the following are characteristics of a data lake?

Accepted Answer

Retains raw data in native format. A data lake retains raw data in its native format, meaning data is ingested without transformation or schema enforcement. This allows storage of structured, semi-structured, and unstructured data as-is, preserving fidelity for future analytics. Unlike a data warehouse, a data lake does not require upfront schema definition, enabling flexible exploration and machine learning workloads.

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Optimized for OLTP

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Stores only structured data

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Enforces ACID transactions

Question 11

Refer to the exhibit. A data analyst notices that direct S3 access to files outside the "incoming/" prefix is blocked. Which data governance principle does this policy enforce?

Accepted Answer

Data access control. The policy blocks direct S3 access to files outside the 'incoming/' prefix, which restricts which users or roles can read or write objects in specific S3 prefixes. This is a classic implementation of data access control, as it enforces permissions based on the resource path, ensuring only authorized operations are allowed on designated data. In AWS S3, such restrictions are typically applied via bucket policies or IAM policies that use conditions like `s3:prefix` to limit access.

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Data colocation

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Data retention

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Data encryption

Question 12

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 13

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 14

A marketing team needs to store customer feedback from social media posts, including text, images, and emojis. Which data concept is most appropriate for this storage?

Accepted Answer

Unstructured data in a NoSQL document database. Customer feedback from social media includes text, images, and emojis, which lack a predefined schema and are best stored as unstructured data. NoSQL document databases (e.g., MongoDB) store such data in flexible JSON-like documents, allowing each record to have varying fields and data types without requiring a fixed schema.

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Structured data in a relational database

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Unstructured data in a relational database

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Semi-structured data in an XML database

Question 15

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