The answer is to use SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample. This is correct because stratified sampling preserves the proportional representation of key partitions—here, machine_id and date—ensuring that the time series context and class distribution for failure events are maintained, which is critical for accurate feature selection in predictive maintenance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of handling large dataset EDA SageMaker workflows, specifically how Data Wrangler’s built-in sampling strategies balance speed with statistical validity. A common trap is choosing a random row sample, which breaks temporal ordering, or reducing partitions, which loses granularity. Remember the mnemonic: “Stratify by key, don’t randomize the timeline.”
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 working on a predictive maintenance project for a manufacturing company. Sensor data is collected every second from 100 machines and stored in an Amazon S3 bucket as Parquet files, partitioned by machine_id and date. The dataset is massive (10 TB) and contains over 2000 features per machine. The data scientist needs to perform exploratory data analysis to identify which features are most predictive of machine failure. They have access to Amazon SageMaker Studio with a SageMaker Data Wrangler flow. The initial data exploration is taking too long due to the volume of data. The data scientist wants to speed up the analysis without losing accuracy in feature selection. Which course of action is most appropriate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample
Option B is correct because using SageMaker Data Wrangler's sampling capabilities allows faster exploration while preserving statistical properties for feature selection. Option A is wrong because reducing to a single partition loses time series context. Option C is wrong because moving to a smaller instance may cause memory issues. Option D is wrong because random sample of rows may break time series ordering.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Switch to using Amazon EMR with Spark to perform distributed feature selection on the full dataset
Why it's wrong here
Incorrect: This is a valid approach but is more complex and time-consuming than sampling.
✗
Reduce the data to a single partition by concatenating all files and use only one machine's data
Why it's wrong here
Incorrect: This loses cross-machine variability and may bias feature selection.
✓
Use SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample
Why this is correct
Correct: Stratified sampling preserves distribution of key variables and reduces data size.
Related concept
Static NAT maps one inside address to one outside address.
✗
Use Amazon Athena to query a random sample of rows from the dataset
Why it's wrong here
Incorrect: Random sampling may break time series dependencies and is not stratified.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample — Option B is correct because using SageMaker Data Wrangler's sampling capabilities allows faster exploration while preserving statistical properties for feature selection. Option A is wrong because reducing to a single partition loses time series context. Option C is wrong because moving to a smaller instance may cause memory issues. Option D is wrong because random sample of rows may break time series ordering.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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 →
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.
This MLS-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 MLS-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.