- A
Convert the dataset to TFRecord format and use tf.data pipeline with prefetching
TFRecord combines many records into a few large files, and prefetching improves data pipeline efficiency.
- B
Convert the dataset to Parquet format and use Apache Arrow for loading
Why wrong: Parquet is good for analytics but not as efficient for TensorFlow training as TFRecord.
- C
Compress the CSV files and decompress during data loading
Why wrong: Decompression adds overhead and does not solve file fragmentation.
- D
Use a larger instance type with more vCPUs
Why wrong: This does not address I/O bottlenecks from many small files.
Question 66 of 1,000
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 scientist is preparing a large dataset (50 GB) for training a TensorFlow model on SageMaker. The dataset consists of many small CSV files. Training is slow due to I/O bottlenecks. Which data preparation strategy most effectively accelerates training?
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
✓
Convert the dataset to TFRecord format and use tf.data pipeline with prefetching
Option A is correct because TFRecord format stores data in a binary, row-oriented format that TensorFlow's tf.data API can read efficiently, especially with prefetching to overlap data loading with model computation. This eliminates the per-file open/parse overhead of many small CSV files, which is the primary cause of I/O bottlenecks in this scenario.
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.
- ✓
Convert the dataset to TFRecord format and use tf.data pipeline with prefetching
Why this is correct
TFRecord combines many records into a few large files, and prefetching improves data pipeline efficiency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Convert the dataset to Parquet format and use Apache Arrow for loading
Why it's wrong here
Parquet is good for analytics but not as efficient for TensorFlow training as TFRecord.
- ✗
Compress the CSV files and decompress during data loading
Why it's wrong here
Decompression adds overhead and does not solve file fragmentation.
- ✗
Use a larger instance type with more vCPUs
Why it's wrong here
This does not address I/O bottlenecks from many small files.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose larger instances (Option D) as a brute-force fix, failing to recognize that the root cause is the small-file I/O pattern, which requires a format change (TFRecord) rather than more compute resources.
Detailed technical explanation
How to think about this question
TFRecord files are designed for sequential reads, allowing TensorFlow to use memory-mapped I/O and internal buffering to minimize disk seeks. The tf.data pipeline with prefetching (e.g., dataset.prefetch(tf.data.AUTOTUNE)) overlaps data loading with GPU computation, hiding latency. In practice, converting 50 GB of small CSVs into a single TFRecord shard can reduce training time by 3–10x compared to raw CSV loading.
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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Data Preparation for Machine Learning — study guide chapter
Learn the concepts, then practise the questions
- →
Data Preparation for Machine Learning practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
1,000 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Convert the dataset to TFRecord format and use tf.data pipeline with prefetching — Option A is correct because TFRecord format stores data in a binary, row-oriented format that TensorFlow's tf.data API can read efficiently, especially with prefetching to overlap data loading with model computation. This eliminates the per-file open/parse overhead of many small CSV files, which is the primary cause of I/O bottlenecks in this scenario.
What should I do if I get this MLA-C01 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More MLA-C01 practice questions
- A team is using SageMaker Pipelines to train a model. The pipeline has multiple steps: data processing, training, evalua…
- A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to ac…
- A machine learning engineer sees the above error in Amazon CloudWatch Logs for a SageMaker endpoint. What is the most li…
- A data scientist has trained a model that achieves 95% accuracy on the training set but only 70% on the test set. Which…
- Refer to the exhibit. A data scientist reviews the output of a SageMaker training job. The model has 95% training accura…
- A team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively explor…
Last reviewed: Jun 24, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.