Question 338 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A team is using Amazon SageMaker Processing for data preprocessing. They have a Parquet dataset in Amazon S3. Which configuration will provide the most efficient reading of the dataset during processing?
⚠ Common exam trap
AWS often tests the misconception that many small files improve parallelism, but in distributed systems like Spark on SageMaker, small files increase S3 API call overhead and scheduler latency, making larger Parquet files (e.g., 128 MB–1 GB) far more efficient for reading.
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
✓
Read the Parquet files directly using SparkSession.read.parquet
SageMaker Processing natively integrates with Apache Spark, and reading Parquet files directly via `SparkSession.read.parquet` leverages columnar storage, predicate pushdown, and compression (e.g., Snappy) to minimize I/O and deserialization overhead. This approach is far more efficient than text-based or format-conversion methods, as Parquet is optimized for analytical workloads and preserves schema information.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Read the Parquet files as text using SparkContext.textFile
Why it's wrong here
Loses the columnar benefits of Parquet.
- ✗
Split the dataset into many small Parquet files (e.g., 1 MB each)
Why it's wrong here
Too many small files cause I/O overhead.
- ✗
Convert the Parquet files to CSV before processing
Why it's wrong here
CSV is larger and slower to read than Parquet.
- ✓
Read the Parquet files directly using SparkSession.read.parquet
Why this is correct
Leverages Parquet's efficiency and schema.
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 |
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Last reviewed: Jun 30, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLA-C01 exam.
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