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FC0-U71 Practice Question: A junior developer at a small company that uses a…
You are a junior developer at a small company that uses a monolithic web application written in Python. The application runs on a single server and uses a MySQL database. Recently, the application has become slow during peak hours. The operations team reports high CPU usage on the server. You suspect that the database queries are not optimized. You propose to refactor the application to use a caching layer. However, your manager is concerned about adding complexity and suggests that you first optimize the most frequently executed queries. You review the code and find that the most frequent query selects all columns from a large table without a WHERE clause. The table has over 1 million rows. Which course of action should you take?
⚠ Common exam trap
A common mix-up: candidates assume adding an index always speeds up any query, but without a WHERE clause, an index is never used—the database still must scan the entire table.
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
✓
Modify the query to include a WHERE clause that filters on an indexed column.
The query selects all columns from a large table without a WHERE clause, which forces a full table scan. Adding a WHERE clause that filters on an indexed column reduces the number of rows scanned, dramatically improving performance without introducing the complexity of a caching layer. This directly addresses the high CPU usage caused by inefficient query execution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement a caching layer using Redis.
Why it's wrong here
Caching is beneficial but adds complexity; optimizing the query is a more fundamental fix.
- ✗
Upgrade the server to have more CPU and RAM.
Why it's wrong here
Upgrading hardware is a temporary solution and does not fix inefficient queries.
- ✗
Add an index on all columns used in the query.
Why it's wrong here
Without a WHERE clause, an index is not used; the query still scans the entire table.
- ✓
Modify the query to include a WHERE clause that filters on an indexed column.
Why this is correct
Adding a WHERE clause reduces the amount of data retrieved, improving performance.
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