DA0-001 Practice Test 20 — 15 Questions

Question 1

A data engineer is designing an ETL pipeline to extract sales data from a legacy on-premise database and load it into a cloud data warehouse. The database is slow and queries during business hours affect performance. Which extraction strategy minimizes impact?

Accepted Answer

Incremental extraction using Change Data Capture (CDC). Incremental extraction using Change Data Capture (CDC) minimizes impact on the legacy on-premise database by reading only the changed rows (inserts, updates, deletes) from transaction logs or change tables, rather than issuing heavy SELECT queries. This avoids full table scans or frequent queries during business hours, preserving database performance for operational workloads.

Answer

Query the database with SELECT * every hour

Answer

Full table extraction nightly

Answer

Use a database log shipping

Question 2

During data acquisition, an analyst notices that the data from an external vendor has inconsistent date formats. What is the first step the analyst should take?

Accepted Answer

Perform data profiling. Data profiling is the initial step to understand the structure, quality, and issues in the data. Rejecting or transforming without profiling may lead to errors, and contacting the vendor is premature without understanding the scope.

Answer

Contact the vendor to request corrected data

Answer

Immediately transform dates to a standard format

Answer

Reject the entire dataset

Question 3

An analyst is reviewing the above SQL query used to acquire data. What does this query retrieve?

Accepted Answer

Customers who placed more than 5 orders in 2023. The SQL query uses a HAVING clause with COUNT(*) > 5 to filter customers who placed more than 5 orders in 2023. The WHERE clause restricts records to the year 2023, and the GROUP BY customer_id aggregates orders per customer. The condition '> 5' explicitly excludes customers with exactly 5 or fewer orders, making option A correct.

Answer

All customers who placed at least 5 orders in 2023

Answer

The total number of orders per customer in 2023

Answer

Customers who placed exactly 5 orders in 2023

Question 4

A company wants to collect real-time clickstream data from its website. Which acquisition method is most suitable?

Accepted Answer

Streaming API. A streaming API is the most suitable method for collecting real-time clickstream data because it enables continuous, low-latency ingestion of events as they occur. Unlike batch or manual methods, a streaming API (e.g., using WebSockets or HTTP/2 Server-Sent Events) pushes each click event immediately to the data pipeline, satisfying the real-time requirement.

Answer

Web scraping

Answer

Batch processing nightly

Answer

Manual entry

Question 5

A marketing analyst needs to combine customer data from a CRM database with social media engagement data from a third-party API. Which data acquisition method is most appropriate?

Accepted Answer

API integration. API integration is the most appropriate method because it allows the analyst to programmatically retrieve structured social media engagement data directly from the third-party service's RESTful or GraphQL API endpoints. This approach ensures real-time or near-real-time data synchronization, supports authentication (e.g., OAuth 2.0), and returns data in standardized formats like JSON or XML, which can be directly ingested into the CRM system without manual intervention.

Answer

Web scraping

Answer

Manual data entry

Answer

Batch file upload

Question 6

Which THREE data sources are suitable for web scraping? (Select three.)

Accepted Answer

HTML pages. HTML pages are suitable for web scraping because they contain structured or semi-structured data in markup format that can be parsed using libraries like BeautifulSoup or Scrapy. Web scrapers extract information from the DOM tree by targeting specific tags, classes, or attributes, making HTML a primary source for scraping.

Answer

CSV files

Answer

Database connections

Question 7

A data team is designing an ETL process to extract data from an operational database daily. The database experiences heavy write loads during business hours. What is the best practice to minimize impact on operations?

Accepted Answer

Use replication or a read replica to extract data. Option B (use a read replica) is best because it offloads the extraction from the primary. Option A (extract from primary) impacts performance. Option C (schedule at midnight) still hits primary. Option D (run during peak) increases load.

Answer

Extract directly from the primary database with high priority

Answer

Run the extraction during peak hours to ensure data freshness

Answer

Schedule extraction at midnight when load is low

Question 8

A data analyst is tasked with gathering data from a legacy system that only exports CSV files. The files contain headers but no data types. Which tool would best facilitate initial data exploration?

Accepted Answer

Python pandas. Option A is correct because Python pandas provides powerful data structures and functions for exploring and manipulating CSV data, including detecting data types. Option B is wrong because SQL database requires importing data first. Option C is wrong because Hadoop is overkill for CSV exploration. Option D is wrong because Tableau is visualization-focused and not ideal for initial data exploration without pre-processing.

Answer

Hadoop

Answer

Tableau

Answer

SQL database

Question 9

Refer to the exhibit. What is the most likely cause of the extraction failure?

Accepted Answer

The network firewall is blocking the port. Option B is correct because connection timeouts with consistent 30-second delays suggest the network firewall is blocking the port, causing the connection to hang until timeout. Option A is wrong because if the server were down, the error would be connection refused immediately. Option C is wrong because a complex query would cause a slow query, not a connection timeout. Option D is wrong because a locked table would cause a lock wait timeout, not a connection timeout.

Answer

The source table is locked

Answer

The extraction query is too complex

Answer

The database server is down

Question 10

A retail company's data analytics team needs to acquire point-of-sale (POS) transaction data from 200 stores daily. Each store sends a CSV file via email at the end of the day. The files often arrive late, have inconsistent column names (e.g., "StoreID", "Store_ID", "store_id"), and occasionally contain corrupted rows. The team manually processes these files, leading to frequent errors and delays. The company wants to automate the acquisition process to ensure data is available by 9 AM the next business day with high quality. Which approach best addresses these issues?

Accepted Answer

Create a script to automatically download email attachments, validate and standardize columns, and flag corrupted rows for review. Option A is correct because it directly addresses all three issues: automating the retrieval of email attachments (handling late arrivals), standardizing inconsistent column names via a script (e.g., mapping 'StoreID', 'Store_ID', 'store_id' to a canonical schema), and implementing validation logic to flag corrupted rows for manual review. This approach ensures data is processed reliably by 9 AM without manual intervention, meeting the automation and quality requirements.

Answer

Hire a data entry contractor to manually check and re-enter data

Answer

Ask stores to use a standardized web form to enter data directly into a cloud database

Answer

Implement a VPN so stores can connect to the central database and write transactions in real time

Question 11

A data analyst needs to identify duplicate customer records. Which TWO methods are commonly used? (Select two.)

Accepted Answer

Fuzzy matching using Levenshtein distance. Fuzzy matching using Levenshtein distance (Option A) is correct because it measures the edit distance between two strings, allowing identification of duplicates even when there are minor typographical differences, such as 'Jon Smith' vs. 'John Smith'. This is essential for deduplicating customer records where names, addresses, or other fields may have slight variations without being exact matches.

Answer

Visual inspection of random sample

Answer

Using a hash function on primary key

Answer

Exact match on all fields

Question 12

A company receives daily sales data in CSV format. The data includes a 'Date' column in MM/DD/YYYY format. To load this into a database that expects YYYY-MM-DD, the analyst should:

Accepted Answer

Use a data transformation tool to convert the date format during ETL. Option B (use a transformation tool) is the standard practice during ETL. Option A (manually edit) is inefficient and error-prone. Option C (change schema) may cause other issues. Option D (ignore) loses data integrity.

Answer

Manually edit the CSV files before loading

Answer

Change the database schema to accept MM/DD/YYYY

Answer

Ignore the date column and use a default date

Question 13

A data scientist is merging retail transaction data from online and in-store sources. Which THREE steps are required to ensure data consistency?

Accepted Answer

Ensure product IDs are standardized across sources. Options A (standardize product IDs), C (convert currency), and D (synchronize timestamps) are essential. Option B (use only store location) is insufficient. Option E (remove transactions with missing customer ID) may discard useful data.

Answer

Remove all transactions with missing customer ID

Answer

Merge data using only store location

Question 14

A data analyst is extracting data from a relational database using SQL. Which clause is essential for limiting the rows retrieved to only those needed?

Accepted Answer

WHERE. Option A is correct because the WHERE clause filters rows based on conditions. Option B is wrong because GROUP BY groups rows for aggregation. Option C is wrong because ORDER BY sorts results. Option D is wrong because HAVING filters groups after aggregation.

Answer

GROUP BY

Answer

ORDER BY

Answer

HAVING

Question 15

A data analyst is tasked with collecting data from multiple spreadsheets provided by different departments. Each spreadsheet has different column names and formats. What is the best first step?

Accepted Answer

Develop a data dictionary and standardize column names. Developing a data dictionary and standardizing column names ensures consistency across all data sources before loading, reducing errors and facilitating integration. Immediately loading data can cause inconsistencies. Discarding mismatched data loses potentially valuable information. Using a machine learning model is an unnecessary and complex first step.

Answer

Discard any mismatched data

Answer

Use a machine learning model to clean data

Answer

Immediately load all data into a database