DA0-001 Mining and Acquiring Data Practice Test 1 — 15 Questions

Question 1

A data analyst is pulling data from a production database for a report. The database contains customer orders with a column 'order_date'. The analyst notices that some orders have dates in the future. Which data quality issue does this represent?

Accepted Answer

Violation of business rules. Option D is correct because future order dates violate a business rule that order_date must be in the past or present. This is a classic data integrity issue where the data does not conform to domain-specific constraints, such as 'order_date <= CURRENT_DATE'. The analyst should flag this as a violation of business rules, not a data type or consistency problem.

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Invalid data type

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Inconsistent data

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Missing data

Question 2

A data engineer is designing a data pipeline to ingest streaming data from IoT sensors. The sensors send data every second, and the pipeline must handle bursts of up to 10,000 messages per second. Which approach is most appropriate for capturing this data before processing?

Accepted Answer

Use a message queue to buffer the incoming data. Option C is correct because a message queue (e.g., Apache Kafka, Amazon Kinesis, or RabbitMQ) provides an asynchronous buffer that decouples the high-velocity ingestion (up to 10,000 messages/second) from downstream processing. This allows the pipeline to absorb burst traffic without overwhelming the processing layer, ensures data durability, and supports replayability in case of failures.

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Directly write each message to a relational database

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Load directly into a data warehouse

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Store data in flat files and process in nightly batches

Question 3

A data analyst needs to combine two datasets: one contains customer information (customer_id, name, address) and the other contains order information (order_id, customer_id, order_date). The analyst wants to include all customers, even those who have not placed orders. Which type of join should be used?

Accepted Answer

LEFT JOIN. A LEFT JOIN returns all rows from the left table (customers) and the matching rows from the right table (orders). If a customer has no orders, the order columns will contain NULLs. This satisfies the requirement to include all customers, even those without orders.

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FULL OUTER JOIN

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INNER JOIN

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RIGHT JOIN

Question 4

A data analyst is tasked with extracting data from a legacy system that outputs fixed-width text files. The analyst needs to parse these files into a structured format. Which tool or method is most appropriate for this task?

Accepted Answer

A scripting language such as Python. Python is the most appropriate choice because fixed-width text files require precise column slicing based on character positions, which Python's string slicing and libraries like `struct` or `pandas.read_fwf` handle natively. Unlike graphical ETL tools or spreadsheets, Python provides programmatic control to define exact field widths, handle edge cases like missing delimiters, and process large files efficiently without manual intervention.

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A spreadsheet application

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An ETL tool with a graphical interface

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SQL

Question 5

A company is merging two databases from different departments. In Database A, customer IDs are integers. In Database B, customer IDs are alphanumeric strings. To merge, the data analyst must reconcile these differences. Which step should be taken first?

Accepted Answer

Perform data profiling to understand the ID formats and relationships. Option C is correct because data profiling is the essential first step before any transformation or mapping. It allows the analyst to examine the actual formats, patterns, and relationships in both ID columns (e.g., whether Database B's alphanumeric IDs contain embedded numeric sequences or consistent prefixes). Without profiling, any conversion or mapping would be based on assumptions that could lead to data loss or incorrect merges.

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Drop the ID column and use a surrogate key

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Convert all IDs to integers using CAST

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Create a mapping table based on the first character

Question 6

A data analyst needs to extract data from an API that returns JSON. The analyst wants to convert the JSON output into a tabular format for analysis. Which function in a scripting language is commonly used for this purpose?

Accepted Answer

json_normalize(). Option D is correct because `json_normalize()` is a function in the pandas library specifically designed to flatten semi-structured JSON data (including nested lists and dictionaries) into a tabular DataFrame. This makes it the ideal tool for converting API responses with complex nesting into rows and columns for analysis, unlike simpler JSON parsing functions.

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json.loads()

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to_csv()

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read_json()

Question 7

A data analyst is building a dataset from multiple sources and needs to ensure data quality. During the data acquisition phase, which activity is most important to perform?

Accepted Answer

Data profiling. Data profiling is the most important activity during the data acquisition phase because it involves examining source data to understand its structure, content, and quality issues before integration. This step identifies missing values, data types, duplicates, and inconsistencies early, preventing downstream errors in analysis. Without profiling, subsequent cleaning and modeling may be based on flawed assumptions about the data.

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Data visualization

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Data cleaning

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Data modeling

Question 8

An organization needs to acquire data from a third-party vendor. The data will be used for regulatory reporting. Which of the following should be the primary consideration before acquiring the data?

Accepted Answer

Legal and compliance requirements. When acquiring data for regulatory reporting, legal and compliance requirements must be the primary consideration because the data must adhere to specific laws (e.g., GDPR, HIPAA, SOX) and industry regulations. Failing to ensure compliance can result in legal penalties, fines, or rejection of the report by regulatory bodies. This overrides technical or cost concerns, as non-compliant data is unusable for its intended purpose.

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Volume of data

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Data format

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Cost of the data

Question 9

A data analyst is using SQL to extract data. The analyst wants to retrieve all records from a table named 'sales' where the 'amount' column is greater than 100. Which SQL clause should be used?

Accepted Answer

WHERE. The WHERE clause in SQL is used to filter records based on a specified condition, such as 'amount > 100'. It is applied directly to the rows in the 'sales' table before any grouping or ordering, making it the correct choice for retrieving only records where the amount exceeds 100.

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ORDER BY

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GROUP BY

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HAVING

Question 10

Which TWO of the following are common methods for acquiring data from external sources?

Accepted Answer

Public APIs. Public APIs (C) are a common method for acquiring data from external sources because they provide a standardized, programmatic interface (often RESTful over HTTP/HTTPS) for requesting and receiving structured data, such as JSON or XML, from third-party services like social media platforms or weather services. Web scraping (D) is another common method that involves programmatically extracting data from web pages by parsing HTML or DOM structures, often using tools like BeautifulSoup or Selenium, when no API is available.

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Data warehousing

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Manual data entry

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Direct database connection to an internal server

Question 11

Which THREE of the following are best practices when performing data extraction for a data pipeline?

Accepted Answer

Implementing error handling and logging. Option B is correct because implementing error handling and logging is a critical best practice in data pipeline extraction. It ensures that failures (e.g., network timeouts, authentication errors, or schema mismatches) are captured and can be diagnosed without data loss or silent corruption, which is essential for maintaining pipeline reliability and auditability.

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Performing a full refresh every time

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Ignoring data quality issues during extraction

Question 12

Which TWO of the following are valid SQL clauses used to filter and sort data?

Accepted Answer

WHERE. The WHERE clause is used to filter rows based on specified conditions, while the ORDER BY clause sorts the result set in ascending or descending order. Both are standard SQL clauses for data filtering and sorting, respectively.

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DELETE

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UPDATE

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INSERT

Question 13

What is the primary purpose of the HAVING clause in the query shown?

Accepted Answer

Filter groups after aggregation. The HAVING clause is used to filter groups after the GROUP BY clause has aggregated the data. In SQL, WHERE filters individual rows before aggregation, while HAVING applies conditions to the results of aggregate functions like SUM, COUNT, or AVG. Option D is correct because the query uses HAVING to restrict which grouped results appear in the final output.

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Sort the results in descending order

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Join two tables

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Filter rows before grouping

Question 14

A data analyst sees this error in the ETL logs. What is the most likely cause?

Accepted Answer

The materialized view log was updated after the last refresh. The error indicates that the materialized view's underlying data has changed since its last refresh, specifically because the materialized view log was updated. Materialized views rely on logs to track changes for fast refreshes; if the log is updated after the last refresh, the view's snapshot becomes stale and cannot be incrementally refreshed without a complete refresh. This is a common cause of refresh failures in Oracle databases.

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The source table was dropped

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The analyst does not have permission to refresh the view

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There is a network connection timeout

Question 15

A data engineer is configuring access to a data lake in Amazon S3. What does the JSON policy shown allow?

Accepted Answer

Read objects from the bucket. The JSON policy shown grants the `s3:GetObject` action, which allows reading objects from the specified S3 bucket. This is a standard AWS IAM policy that explicitly permits the `GetObject` API call, enabling the data engineer to retrieve objects from the data lake.

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Change user permissions

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Delete objects from the bucket

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Write objects to the bucket