MLS-C01 Exploratory Data Analysis Practice Question
Exhibit
Refer to the exhibit.
```
# S3 Select query result on a CSV file
SELECT * FROM s3object s WHERE s."age" > 30 AND s."city" = 'New York'
# Result:
{
"Payload": [
{"Records": {"Payload": "name,age,city\nAlice,35,New York\nBob,40,New York\n"}},
{"Stats": {"Details": {"BytesScanned": 1024, "BytesProcessed": 512, "BytesReturned": 64}}}
]
}
```Refer to the exhibit. A data scientist ran an S3 Select query on a large CSV file stored in Amazon S3. The output shows only 2 records returned, but the data scientist expected thousands. The file size is 10 GB. What is the MOST likely reason for the small result set?
⚠ Common exam trap
The MLS-C01 exam often tests the nuance that S3 Select does not automatically trim or normalize string data, so candidates mistakenly assume the query engine handles such common data quality issues.
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 city column may have leading/trailing spaces or case differences.
S3 Select performs exact string matching by default, so if the WHERE clause filters on the city column, any leading/trailing spaces or case differences will cause mismatches, returning far fewer rows than expected. The query likely used a literal like 'New York' while the data contains ' New York ' or 'new york', resulting in only 2 matches instead of thousands.
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 file needs to be indexed by S3 Select before querying.
Why it's wrong here
S3 Select does not require indexes.
- ✓
The city column may have leading/trailing spaces or case differences.
Why this is correct
String comparison is exact; variations cause mismatches, reducing results.
- ✗
The CSV file contains nested arrays that S3 Select cannot parse.
Why it's wrong here
CSV does not have nested arrays; S3 Select supports CSV.
- ✗
S3 Select does not support the WHERE clause on CSV files.
Why it's wrong here
S3 Select supports WHERE clause on CSV.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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