The correct combination of SageMaker Data Wrangler features for exploratory data analysis is to generate a data quality report, view histograms, and create scatter plots for selected features. This is correct because the data quality report provides summary statistics and missing value analysis, histograms reveal the distribution of individual features, and scatter plots expose relationships between variables—all core EDA tasks. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between EDA features and data preparation or transformation steps. A common trap is confusing transformation actions like imputation or one-hot encoding with EDA, which is about understanding data, not modifying it. Remember the EDA mantra: inspect before you transform. A useful memory tip is “Quality, Shape, Relationship”—the data quality report covers quality, histograms show shape, and scatter plots reveal relationships.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 team is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a large dataset stored in S3. The dataset contains missing values, outliers, and categorical variables with high cardinality. The team wants to understand data distributions and relationships before modeling. Which combination of Data Wrangler features should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Generate a data quality report, view histograms, and create scatter plots for selected features.
Option D is correct because Data Wrangler's data quality report provides summary statistics and missing value analysis, and the histogram visualization shows distributions. Scatter plots reveal relationships between variables. Option A is incorrect because Data Wrangler does not include correlation heatmaps directly. Option B is incorrect because imputation and one-hot encoding are transformations, not EDA steps. Option C is incorrect because handling missing values is part of data preparation, not initial EDA.
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.
✓
Generate a data quality report, view histograms, and create scatter plots for selected features.
Why this is correct
Data quality report provides summary statistics and missing values; histograms and scatter plots show distributions and relationships.
Related concept
Static NAT maps one inside address to one outside address.
✗
Drop rows with missing values and visualize box plots for numerical features.
Why it's wrong here
Dropping rows may introduce bias; initial EDA should include missing value analysis.
✗
Use imputation to handle missing values and one-hot encoding for categorical features.
Why it's wrong here
These are data preparation steps, not EDA.
✗
Generate a data quality report and a correlation heatmap.
Why it's wrong here
Data Wrangler does not produce correlation heatmaps natively.
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: Generate a data quality report, view histograms, and create scatter plots for selected features. — Option D is correct because Data Wrangler's data quality report provides summary statistics and missing value analysis, and the histogram visualization shows distributions. Scatter plots reveal relationships between variables. Option A is incorrect because Data Wrangler does not include correlation heatmaps directly. Option B is incorrect because imputation and one-hot encoding are transformations, not EDA steps. Option C is incorrect because handling missing values is part of data preparation, not initial EDA.
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.
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Question Discussion
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