DA0-001 Practice Test 35 — 15 Questions

Question 1

An analyst needs to combine two datasets from different sources that share a common key but have different levels of granularity. Dataset A has daily sales per store, Dataset B has hourly foot traffic per store. The analyst wants to analyze correlation. Which approach is appropriate?

Accepted Answer

Aggregate Dataset B to daily level before merging. Aggregating Dataset B (hourly foot traffic) to the daily level ensures both datasets share the same granularity before merging on the common key (store and date). This allows a valid correlation analysis between daily sales and daily foot traffic without introducing artificial patterns or data duplication. Merging at mismatched granularities would violate the assumption that each row represents a comparable unit of observation.

Answer

Use an outer join and keep all rows

Answer

Disaggregate Dataset A to hourly level by dividing daily sales by hours

Answer

Join on store and date without aggregation

Question 2

Which THREE are best practices for data profiling during acquisition? (Choose three.)

Accepted Answer

Check for completeness. Checking for completeness (Option B) is a best practice during data acquisition because it ensures that all required fields and records are present before further processing. Incomplete data can lead to incorrect analysis or failed transformations, so profiling for missing values or nulls is a fundamental validation step.

Answer

Immediately normalize data

Answer

Skip validation for trusted sources

Question 3

A data analyst is tasked with combining customer data from a CRM system and a billing system. The CRM uses a GUID for customer ID, while billing uses an integer. Which approach should the analyst use to ensure a reliable merge?

Accepted Answer

Standardize the customer ID format and use it as the join key.. Option A is correct because standardizing the customer ID format (e.g., converting the billing integer to a GUID or mapping both to a common string key) ensures a consistent join key across heterogeneous systems. This eliminates type mismatch errors and guarantees that each customer record can be matched reliably, as GUIDs are globally unique and integers are typically sequential, so direct comparison would fail without transformation.

Answer

Use the customer name as the join key.

Answer

Merge using a cross-join and then filter manually.

Answer

Perform a fuzzy match on the customer address.

Question 4

A data analyst needs to merge two customer tables from different sources. One table uses 'CUST_ID' as the primary key, the other uses 'CustomerID'. To ensure accurate merging, the analyst should first:

Accepted Answer

Normalize the key column names to a common format. Normalizing key column names to a common format (Option B) is the correct first step because the merge operation requires a consistent join key. Without aligning 'CUST_ID' and 'CustomerID' to a single name and data type, the database or ETL tool will treat them as different columns, resulting in a cross join or an error. This step ensures referential integrity and enables an accurate inner or outer join based on the primary key.

Answer

Perform a fuzzy match on names

Answer

Remove duplicate rows from both tables

Answer

Aggregate data by region

Question 5

A company is acquiring social media data via a public API. Which TWO considerations are important for ensuring ethical and legal compliance?

Accepted Answer

Anonymize personal identifiable information (PII) before storage. Option C is correct because anonymizing PII before storage is a fundamental data privacy requirement under regulations like GDPR and CCPA. When acquiring data via a public API, the company must ensure that personal identifiers (e.g., names, email addresses, IP addresses) are removed or obfuscated to prevent re-identification, reducing legal liability and ethical risk.

Answer

Share raw data with third parties for additional insights

Answer

Use the data for any internal analysis without restrictions

Answer

Cache data indefinitely to avoid repeated API calls

Question 6

A financial institution needs to acquire credit transaction data from multiple sources while ensuring compliance with data privacy regulations. What is the most critical step?

Accepted Answer

Data anonymization during extraction. Data anonymization during extraction is the most critical step because it ensures that personally identifiable information (PII) is irreversibly masked or removed before the data enters the processing pipeline, directly addressing compliance with regulations such as GDPR and PCI DSS. Without this step, even if other measures are applied later, the initial exposure of sensitive data violates privacy mandates and increases breach risk.

Answer

Data replication for redundancy

Answer

Data enrichment with external sources

Answer

Data compression for storage

Question 7

Refer to the exhibit. What does the query return?

Accepted Answer

Customers who have placed more than 5 orders.. The query uses a HAVING clause with COUNT(*) > 5, which filters groups (by customer ID) to only those with more than 5 orders. The GROUP BY customer ID ensures the count is per customer, so the result is customers who have placed more than 5 orders. Option C is correct because the condition is strictly greater than 5, not at least 5.

Answer

All orders grouped by customer ID.

Answer

Customers who have placed at least 5 orders.

Answer

All customers who have placed orders.

Question 8

A data team needs to extract data from a legacy system that only supports flat file exports. Which data acquisition method is most appropriate?

Accepted Answer

File transfer via SFTP. The legacy system only supports flat file exports, meaning it cannot provide direct database or API access. SFTP (SSH File Transfer Protocol) is the most appropriate method because it securely transfers flat files over a network, aligning with the system's export capabilities while ensuring data integrity and encryption during transit.

Answer

Database replication

Answer

API call

Answer

Web scraping

Question 9

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 10

A data pipeline log shows the above error. Which data transformation should be applied during acquisition?

Accepted Answer

Preprocess the string to remove non-numeric characters, then convert to DECIMAL. Option B is correct because the error indicates that the pipeline encountered a string with non-numeric characters (e.g., '$1,234.56') when trying to load it into a DECIMAL column. Preprocessing the string to remove non-numeric characters (like currency symbols, commas) before conversion ensures the data is clean and parseable, which is a standard data transformation during acquisition to handle dirty source data.

Answer

Skip rows that cause errors

Answer

Use CAST(transaction_amount AS DECIMAL(10,2)) in SQL

Answer

Change the target column type to VARCHAR

Question 11

A financial institution is merging transaction data from two different systems. System A stores currency amounts as integers in cents, and System B stores as decimals in dollars. What is the best way to integrate the data?

Accepted Answer

Convert System A amounts to dollars by dividing by 100.. Option A is correct because converting System A's integer cents to dollars by dividing by 100 ensures both datasets share a consistent unit (dollars) and numeric data type (decimal). This direct transformation eliminates ambiguity in aggregation and reporting, as financial calculations require uniform precision and scale. Using a transformation layer or storing as strings would introduce unnecessary complexity or risk of rounding errors.

Answer

Keep both as is and use a transformation layer.

Answer

Store all amounts as strings to preserve precision.

Answer

Convert System B amounts to cents by multiplying by 100.

Question 12

A data engineer is tasked with acquiring data from a third-party vendor that provides daily file drops via SFTP. The files are large (10 GB each). The pipeline must load data into a data warehouse. Which approach optimizes for speed and reliability?

Accepted Answer

Stream the file directly from SFTP into warehouse using a data pipeline tool. Option B is correct because streaming the file directly from SFTP into the warehouse using a data pipeline tool (e.g., Apache NiFi, Airbyte, or Fivetran) eliminates the intermediate staging step, reducing disk I/O and latency. This approach leverages incremental processing and parallel streams to handle large 10 GB files efficiently, while built-in retry and checkpoint mechanisms ensure reliability against network interruptions.

Answer

Download the file to a staging server, then bulk insert into warehouse

Answer

Have the vendor push data via API instead of SFTP

Answer

Split the file into smaller chunks and load concurrently

Question 13

A marketing analyst wants to segment customers based on purchasing behavior and demographics. The dataset includes continuous variables (spending amount, frequency) and categorical variables (region, gender). The analyst decides to use k-means clustering. What should the analyst do to prepare the data?

Accepted Answer

Standardize continuous variables and one-hot encode categorical variables. Option B is correct because k-means clustering relies on Euclidean distance, which is sensitive to the scale of features. Standardizing continuous variables (e.g., spending amount, frequency) ensures they contribute equally to distance calculations, while one-hot encoding categorical variables (e.g., region, gender) converts them into numerical form without implying ordinal relationships, allowing k-means to process mixed data types correctly.

Answer

Use raw data because k-means works with mixed types

Answer

Apply PCA first to reduce dimensionality

Answer

Remove categorical variables entirely

Question 14

A data analyst at a marketing agency is working with a dataset containing customer demographics, purchase history, and social media engagement metrics. The agency wants to perform sentiment analysis on unstructured social media comments to identify brand perception. The dataset also includes structured fields like age, income, and purchase amounts. The analyst needs to choose a storage and processing platform that can handle both structured and unstructured data efficiently without requiring extensive schema definition upfront. Which platform should the analyst recommend?

Accepted Answer

Data lake. A data lake is the correct choice because it can store both structured data (e.g., age, income, purchase amounts) and unstructured data (e.g., social media comments) in its native format without requiring a predefined schema. This flexibility allows the analyst to ingest raw social media text for sentiment analysis and later apply schema-on-read for structured queries, avoiding the upfront schema definition needed by other platforms.

Answer

Relational database (RDBMS)

Answer

Data warehouse

Answer

NoSQL document database

Question 15

An e-commerce company wants to provide real-time personalized product recommendations based on customer browsing behavior. Currently, they have a traditional data warehouse that processes batch updates every night. The marketing team complains that recommendations are outdated within hours because customers see yesterday's data. The data engineer needs to modify the architecture to support near-real-time analytics. The budget is limited, and the existing warehouse infrastructure must be reused as much as possible. Which architectural change would best meet the requirement?

Accepted Answer

Implement a streaming data pipeline (e.g., Apache Kafka) that feeds a real-time recommendation engine.. Option C is correct because implementing a streaming data pipeline like Apache Kafka enables the ingestion and processing of customer browsing events in near real-time, feeding a dedicated recommendation engine that can update recommendations within seconds or minutes. This approach reuses the existing data warehouse for historical analytics and batch reporting while adding a lightweight streaming layer for low-latency recommendations, aligning with the limited budget and reuse requirement.

Answer

Replace the warehouse with an in-memory database for real-time processing.

Answer

Add more nodes to the warehouse cluster to speed up batch processing.

Answer

Increase the frequency of batch load from nightly to every hour.