DA0-002 Data Acquisition and Preparation — 10 Questions

Question 1

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. Based on the correct answer, the JSON policy shown implicitly grants the `s3:GetObject` action, which allows reading objects from the specified S3 bucket. In AWS IAM, reading objects requires explicit `GetObject` permission, making B the only correct choice.

Answer

Change user permissions

Answer

Delete objects from the bucket

Answer

Write objects to the bucket

Question 2

Which THREE are common challenges when acquiring data from external APIs? (Choose three.)

Accepted Answer

Authentication requirements. Authentication requirements are a common challenge because external APIs typically require valid credentials (e.g., API keys, OAuth 2.0 tokens, or JWT) to access protected resources. Without proper authentication, requests are rejected with HTTP 401 Unauthorized or 403 Forbidden errors, and managing token expiration, refresh cycles, and secure storage adds significant complexity to data acquisition pipelines.

Answer

Consistent data schemas

Answer

Guaranteed uptime

Question 3

Refer to the exhibit. A data engineer is setting up data acquisition from a cloud storage bucket with this policy. What does the policy enforce?

Accepted Answer

Access is restricted to a specific IP range. The bucket policy includes a condition using a source IP address with a CIDR block, which restricts access to requests originating from the specified IP range. This enforces network-level access control, allowing only clients within that IP range to perform the allowed actions (like GET requests) on the bucket. Option D correctly identifies this IP-based restriction.

Answer

Only objects with key 'data-bucket' can be accessed

Answer

Only GET requests are allowed

Answer

The storage container is publicly accessible

Question 4

A data analyst needs to count the number of customers who have placed at least one order. Which SQL query should be used?

Accepted Answer

SELECT COUNT(DISTINCT customer_id) FROM orders. COUNT(DISTINCT customer_id) returns the number of unique customers who have placed orders, correctly counting those with at least one order. Option B sums customer IDs, which is not a valid count. Option A uses invalid syntax. Option C counts all orders including duplicates.

Answer

SELECT DISTINCT COUNT(customer_id) FROM orders

Answer

SELECT SUM(customer_id) FROM orders

Answer

SELECT COUNT(customer_id) FROM orders

Question 5

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. 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.

Answer

Invalid data type

Answer

Inconsistent data

Answer

Missing data

Question 6

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. 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.

Answer

Directly write each message to a relational database

Answer

Load directly into a data warehouse

Answer

Store data in flat files and process in nightly batches

Question 7

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.

Answer

FULL OUTER JOIN

Answer

INNER JOIN

Answer

RIGHT JOIN

Question 8

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.

Answer

A spreadsheet application

Answer

An ETL tool with a graphical interface

Answer

SQL

Question 9

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. 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.

Answer

Drop the ID column and use a surrogate key

Answer

Convert all IDs to integers using CAST

Answer

Create a mapping table based on the first character

Question 10

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(). `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.

Answer

json.loads()

Answer

to_csv()

Answer

read_json()