- A
Data Pipeline: A series of steps to move data from sources to destinations.
Correct: A data pipeline orchestrates the movement and transformation of data between systems.
- B
ETL: Extract, Transform, Load - data is transformed before loading into the target.
Correct: ETL involves extracting data, transforming it, then loading it into the destination.
- C
ELT: Extract, Load, Transform - data is loaded first and then transformed.
Correct: ELT loads raw data first, then transforms it within the target system.
- D
Data Warehouse: A central repository for structured, processed data optimized for analysis.
Correct: Data warehouses store structured data for business intelligence and reporting.
- E
Data Lake: A central repository for structured, processed data optimized for analysis.
Why wrong: Incorrect — this describes a Data Warehouse. Data Lakes store raw, unprocessed data in native format.
- F
ETL: Extract, Load, Transform - data is loaded first and then transformed.
Why wrong: Incorrect — this describes ELT. In ETL, transformation occurs before loading.
Data Pipeline Terminology — Matching Definitions
This PDE practice question tests your understanding of building and operationalizing data processing systems. 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.
Match each data pipeline term to its definition.
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
Data Pipeline: A series of steps to move data from sources to destinations.
Common data pipeline concepts: Data Pipeline moves data; ETL transforms before loading; ELT loads then transforms; Data Warehouse stores structured processed data; Data Lake stores raw data. Distractors swap definitions between ETL/ELT and Data Warehouse/Data Lake.
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.
- ✓
Data Pipeline: A series of steps to move data from sources to destinations.
Why this is correct
Correct: A data pipeline orchestrates the movement and transformation of data between systems.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
ETL: Extract, Transform, Load - data is transformed before loading into the target.
Why this is correct
Correct: ETL involves extracting data, transforming it, then loading it into the destination.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
ELT: Extract, Load, Transform - data is loaded first and then transformed.
Why this is correct
Correct: ELT loads raw data first, then transforms it within the target system.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Data Warehouse: A central repository for structured, processed data optimized for analysis.
Why this is correct
Correct: Data warehouses store structured data for business intelligence and reporting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Data Lake: A central repository for structured, processed data optimized for analysis.
Why it's wrong here
Incorrect — this describes a Data Warehouse. Data Lakes store raw, unprocessed data in native format.
- ✗
ETL: Extract, Load, Transform - data is loaded first and then transformed.
Why it's wrong here
Incorrect — this describes ELT. In ETL, transformation occurs before loading.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this PDE question test?
Building and operationalizing data processing systems — This question tests Building and operationalizing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Data Pipeline: A series of steps to move data from sources to destinations. — Common data pipeline concepts: Data Pipeline moves data; ETL transforms before loading; ELT loads then transforms; Data Warehouse stores structured processed data; Data Lake stores raw data. Distractors swap definitions between ETL/ELT and Data Warehouse/Data Lake.
What should I do if I get this PDE question wrong?
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 11, 2026
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