- A
B-tree index on order_date
Why wrong: A single-column index on order_date would help filter by date but not efficiently combine with customer ID filter.
- B
Hash index on customer_id
Why wrong: A hash index is good for equality lookups on a single column but not for range queries or composite filtering.
- C
Composite index on (order_date, customer_id)
A composite index on both columns allows the database to use the index for queries filtering on both columns, improving performance.
- D
Clustered index on order_id
Why wrong: A clustered index on order_id would order the table by order_id, not beneficial for filtering by date and customer.
Question 943 of 1,000
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 analyst is working with a relational database that contains a table of customer orders. To optimize query performance for a report that filters by order date and customer ID, the analyst wants to create an index. Which type of index would be most effective for queries that filter on both columns?
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
✓
Composite index on (order_date, customer_id)
Option C is correct because a composite B-tree index on (order_date, customer_id) allows the database to efficiently satisfy equality and range predicates on both columns in a single index scan. B-tree indexes support ordered traversal and range lookups, making them ideal for date-based filtering combined with an equality filter on customer_id. This index structure minimizes the number of rows scanned by leveraging the index's leading column for the date range and the second column for the customer ID match.
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.
- ✗
B-tree index on order_date
Why it's wrong here
A single-column index on order_date would help filter by date but not efficiently combine with customer ID filter.
- ✗
Hash index on customer_id
Why it's wrong here
A hash index is good for equality lookups on a single column but not for range queries or composite filtering.
- ✓
Composite index on (order_date, customer_id)
Why this is correct
A composite index on both columns allows the database to use the index for queries filtering on both columns, improving performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Clustered index on order_id
Why it's wrong here
A clustered index on order_id would order the table by order_id, not beneficial for filtering by date and customer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose a single-column index (A or B) thinking it will be sufficient, not realizing that a composite index is required to avoid a 'filter' step that scans many rows after the index lookup.
Detailed technical explanation
How to think about this question
Under the hood, a composite B-tree index stores entries sorted by the first column, then by the second column within ties. This allows the database to perform an index range scan for the date condition and then a simple equality check on customer_id without additional I/O. In PostgreSQL, for example, a B-tree index on (order_date, customer_id) can be used for queries with `WHERE order_date BETWEEN '2024-01-01' AND '2024-01-31' AND customer_id = 123` by seeking to the first matching date and scanning forward until the date condition fails, all while checking customer_id. A real-world scenario is an e-commerce order report that must filter by a specific month and a particular customer; without a composite index, the database might choose a full table scan or an inefficient bitmap scan.
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.
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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: Composite index on (order_date, customer_id) — Option C is correct because a composite B-tree index on (order_date, customer_id) allows the database to efficiently satisfy equality and range predicates on both columns in a single index scan. B-tree indexes support ordered traversal and range lookups, making them ideal for date-based filtering combined with an equality filter on customer_id. This index structure minimizes the number of rows scanned by leveraging the index's leading column for the date range and the second column for the customer ID match.
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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