DA0-001 Comparing and Contrasting Data Concepts Practice Test 2 — 15 Questions

Question 1

A data analyst is troubleshooting a report that shows unusually high sales for a specific product. Upon investigation, the analyst finds that the product was returned by several customers, but the returns were recorded in a separate system and not reflected in the sales data. Which data integration concept was likely missing?

Accepted Answer

Data reconciliation. The core issue is that the sales data and returns data are inconsistent because they were not cross-verified. Data reconciliation is the process of comparing datasets to ensure they are in agreement and identifying discrepancies, such as returns not being reflected in sales figures. Without reconciliation, the analyst would not detect that the high sales number is inflated by unrecorded returns.

Answer

ETL (Extract, Transform, Load)

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Data profiling

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Data governance

Question 2

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.

Answer

The table name 'Products' is misspelled.

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The data types of Price and Quantity are incompatible.

Answer

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

Question 3

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.. The error log from a nightly batch load indicates a primary key violation. This occurs when a row with the same primary key value already exists in the target table from a previous batch load. Since batch loads typically use INSERT operations, attempting to insert a duplicate primary key will raise a constraint violation error, halting the load process.

Answer

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 4

A data analyst is creating a report for a marketing campaign. The campaign data includes customer names, email addresses, and purchase history. Which of the following best describes the 'customer name' data type?

Accepted Answer

Nominal. Customer names are categorical labels that identify individuals without any inherent order or numerical value. This fits the definition of nominal data, which is used for naming or classifying variables. In data analysis, nominal data can be stored as strings and used for grouping or filtering, but arithmetic operations are meaningless.

Answer

Quantitative

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Ordinal

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Discrete

Question 5

Which TWO of the following are considered structured data?

Accepted Answer

A relational database table. Option B is correct because a relational database table stores data in a predefined schema of rows and columns, where each column has a fixed data type. This rigid structure allows for efficient querying, indexing, and relational operations, making it a classic example of structured data.

Answer

A PDF report with free-form text

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A JPEG image of a product

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A JSON file with nested key-value pairs

Question 6

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

Accepted Answer

Defining data owners for each dataset. Option A is correct because 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.

Answer

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 7

Which THREE of the following are characteristics of a relational database?

Accepted Answer

Enforces referential integrity through foreign keys. Option A is correct because relational databases enforce referential integrity through foreign keys, which ensure that relationships between tables remain consistent. A foreign key in a child table must match a primary key value in the parent table, preventing orphaned records and maintaining data integrity.

Answer

Stores data in key-value pairs

Answer

Supports NoSQL document storage

Question 8

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

Accepted Answer

Using a placeholder like 'Unknown' for categorical data. Option A is correct because 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

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Sorting the data to bring missing values to the top

Question 9

A mid-sized e-commerce company stores customer data in a relational database. The database has a table named 'Customers' with columns: CustomerID (primary key), FirstName, LastName, Email, Phone, Address, City, State, ZipCode, and SignUpDate. The company is migrating to a new CRM system that requires a denormalized structure for performance reasons. The new system expects a single table 'CustomerDetails' with columns: CustomerID, FullName (concatenation of first and last name), ContactInfo (JSON object containing email, phone, and address), SignUpDate, and Region (derived from state). The data analyst must design an ETL process to transform the data. During a test run, the analyst notices that some records have missing Phone or Address values. Which of the following is the best approach to handle missing data in the ContactInfo JSON object?

Accepted Answer

Include the missing fields as null in the JSON object.. Option C is correct because representing missing fields as null in the JSON object preserves the data structure and allows downstream systems to explicitly handle null values. This approach maintains data integrity without discarding records or introducing ambiguous placeholder strings that could be misinterpreted as actual data.

Answer

Exclude any record with missing Phone or Address from the migration.

Answer

Set missing values to an empty string in the JSON object.

Answer

Replace missing values with 'N/A' string.

Question 10

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

Question 11

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

Question 12

Match each data quality dimension to its description.

Question 13

Match each data visualization type to its best use case.

Question 14

A company needs to store raw, unprocessed data from IoT sensors for future machine learning experiments. The data is in various formats and schemas are not yet defined. Which storage solution is most appropriate?

Accepted Answer

Data lake. A data lake is the correct choice because it stores raw, unprocessed data in its native format (structured, semi-structured, or unstructured) without requiring a predefined schema. This aligns perfectly with the need to ingest IoT sensor data in various formats for future machine learning experiments, where schemas are not yet defined. Unlike data warehouses or data marts, a data lake supports schema-on-read, allowing the data to be transformed and queried later as needed.

Answer

Data mart

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Data warehouse

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Operational database

Question 15

A financial application requires fast query performance for aggregations on large historical datasets. The schema has many lookup tables. Which schema design is most efficient for this workload?

Accepted Answer

Star schema. The star schema is most efficient for this workload because it denormalizes lookup tables into dimension tables, reducing the number of joins required for aggregations. This design optimizes query performance for large historical datasets by enabling faster full table scans and simpler query plans, which is critical for financial applications needing rapid aggregations.

Answer

Snowflake schema

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Wide table

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Third normal form (3NF)