The answer is to check for duplicate rows based on 'user_id' and 'event_date' and to analyze missing values across the dataset. These two actions directly address core data quality issues during exploratory data analysis (EDA), as duplicates can skew statistical summaries and missing values can introduce bias or break downstream algorithms. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to distinguish between genuine EDA tasks and premature modeling or dimensionality reduction—common traps include assuming you should drop identifiers like user_id immediately or jumping to PCA for quality checks. Remember that EDA is about understanding the data’s structure and integrity before any transformation or training. A useful memory tip: “Dupes and blanks are EDA’s first ranks”—always start by checking for duplicate rows and missing values when assessing data quality.
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 data scientist is performing EDA on a dataset with 1 million rows and 50 features. The dataset includes a column 'user_id' with unique identifiers, a column 'event_date' with timestamps, and other columns. Which TWO actions should the data scientist take to understand data quality issues?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Analyze missing value patterns across columns
Checking for duplicate rows and analyzing missing values are fundamental steps in EDA. Option B is wrong because dropping user_id before analysis may lose information. Option C is wrong because training a model is not part of EDA. Option D is wrong because PCA is not for data quality.
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.
✓
Analyze missing value patterns across columns
Why this is correct
Missing value analysis is key for data quality.
Related concept
Static NAT maps one inside address to one outside address.
✓
Check for duplicate rows based on 'user_id' and 'event_date'
Why this is correct
Duplicates can indicate data quality issues.
Related concept
Static NAT maps one inside address to one outside address.
✗
Drop the 'user_id' column to reduce dimensionality
Why it's wrong here
Should not drop columns before analysis.
✗
Use PCA to reduce dimensions and visualize
Why it's wrong here
PCA is for dimensionality reduction, not data quality.
✗
Train a random forest model to identify feature importance
Why it's wrong here
Model training is not part of EDA.
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 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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.
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: Analyze missing value patterns across columns — Checking for duplicate rows and analyzing missing values are fundamental steps in EDA. Option B is wrong because dropping user_id before analysis may lose information. Option C is wrong because training a model is not part of EDA. Option D is wrong because PCA is not for data quality.
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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