- A
The transformation logic requires proprietary functions not available in the warehouse
Why wrong: If the warehouse lacks the needed functions, ETL with external tools is better.
- B
The business analysts need access to raw data for ad-hoc exploration
ELT loads raw data, allowing analysts to explore it before transformation.
- C
The target data warehouse has massive compute power (e.g., Snowflake) that can handle transformations efficiently
ELT leverages the warehouse's compute for transformations.
- D
Data must be cleansed and validated before loading into the warehouse
Why wrong: If pre-load cleaning is required, ETL is more appropriate.
- E
The source data volume is very large and the warehouse can scale resources on demand
ELT can handle large volumes by scaling warehouse resources.
DA0-001 Data Concepts and Environments Practice Question
This DA0-001 practice question tests your understanding of data concepts and environments. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data engineer is designing a data pipeline for a retail company. The source system is an OLTP database that records sales transactions. The target is a data warehouse used for reporting. The engineer is evaluating whether to use ETL or ELT. Which three factors would favor using ELT over ETL? (Select THREE)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
The business analysts need access to raw data for ad-hoc exploration
Option B is correct because ELT loads raw data into the warehouse first, allowing business analysts to perform ad-hoc exploration directly on the source data without pre-transformation. This flexibility is a key advantage of ELT over ETL, where transformations are applied before loading.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The transformation logic requires proprietary functions not available in the warehouse
Why it's wrong here
If the warehouse lacks the needed functions, ETL with external tools is better.
- ✓
The business analysts need access to raw data for ad-hoc exploration
Why this is correct
ELT loads raw data, allowing analysts to explore it before transformation.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
The target data warehouse has massive compute power (e.g., Snowflake) that can handle transformations efficiently
Why this is correct
ELT leverages the warehouse's compute for transformations.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Data must be cleansed and validated before loading into the warehouse
Why it's wrong here
If pre-load cleaning is required, ETL is more appropriate.
- ✓
The source data volume is very large and the warehouse can scale resources on demand
Why this is correct
ELT can handle large volumes by scaling warehouse resources.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the direction of data flow, mistakenly thinking that ELT requires transformations before loading, when in fact ELT defers transformations until after data is in the warehouse.
Detailed technical explanation
How to think about this question
ELT leverages the compute power of modern cloud data warehouses like Snowflake or Amazon Redshift to perform transformations after loading, which can be more scalable for large datasets. Under the hood, ELT uses SQL-based transformations that can be parallelized across distributed nodes, whereas ETL often relies on separate processing engines like Apache Spark or Informatica. A real-world scenario is a retail company ingesting terabytes of sales data daily; ELT allows them to load raw JSON or Parquet files and transform them on-demand using warehouse-native functions like Snowflake's UNPIVOT or Redshift's COPY command.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this DA0-001 question test?
Data Concepts and Environments — This question tests Data Concepts and Environments — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The business analysts need access to raw data for ad-hoc exploration — Option B is correct because ELT loads raw data into the warehouse first, allowing business analysts to perform ad-hoc exploration directly on the source data without pre-transformation. This flexibility is a key advantage of ELT over ETL, where transformations are applied before loading.
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This DA0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-001 exam.
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