The answer is to impute missing total charges with zero because the missing values occur exclusively for customers with tenure equal to zero, meaning they are new customers who have not yet been billed. In customer churn prediction, total charges represent cumulative billing, so a tenure of zero logically implies no charges have accrued, making zero the only accurate imputation. This question tests your understanding of domain-specific missing data mechanisms on the AWS Certified Machine Learning Specialty MLS-C01 exam, where the trap is to default to mean imputation or row deletion—both of which would bias the model by either inflating charges for new customers or removing an entire segment from the dataset. A common memory tip is "zero tenure, zero charges": if a customer hasn't stayed a full month, their total charges must be zero, not an average or a predicted value.
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 customer churn prediction project for a telecom company. The dataset contains 50,000 records with 25 features, including 'tenure' (number of months customer stayed), 'monthly_charges', 'total_charges', 'contract_type' (month-to-month, one year, two year), 'payment_method', and a target 'churn' (Yes/No). The data is stored in an S3 bucket as a single CSV file. The scientist uses Amazon SageMaker Data Wrangler to perform EDA. After importing the data, the scientist notices that the 'total_charges' column has many missing values (about 20% of rows). The scientist suspects that missing values occur only for customers with tenure = 0 (new customers). After verifying that suspicion, the scientist wants to handle the missing values appropriately. Which course of action should the scientist take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Impute missing total_charges with 0, since missing values correspond to customers with tenure=0.
Option D is correct because if total_charges is missing only for tenure=0, it means those customers have not been billed yet, so total_charges should be 0. Imputing with 0 is appropriate. Option A is wrong because dropping rows with missing total_charges would remove all new customers, biasing the dataset. Option B is wrong because imputing with mean would assign incorrect values to new customers. Option C is wrong because using a model to predict missing values is overkill and may introduce error when the true value is known to be 0.
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.
✗
Use a regression model to predict total_charges based on other features.
Why it's wrong here
Predicting total_charges is unnecessary when domain knowledge indicates it should be 0 for new customers.
✗
Impute missing total_charges with the mean of non-missing values.
Why it's wrong here
Mean imputation would assign a value that is not appropriate for new customers (likely 0).
✗
Drop all rows with missing total_charges to avoid bias.
Why it's wrong here
Dropping rows removes valid new customers, reducing sample size and introducing selection bias.
✓
Impute missing total_charges with 0, since missing values correspond to customers with tenure=0.
Why this is correct
Given the pattern, total_charges should be 0 for new customers; imputing with 0 preserves data integrity.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 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.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Impute missing total_charges with 0, since missing values correspond to customers with tenure=0. — Option D is correct because if total_charges is missing only for tenure=0, it means those customers have not been billed yet, so total_charges should be 0. Imputing with 0 is appropriate. Option A is wrong because dropping rows with missing total_charges would remove all new customers, biasing the dataset. Option B is wrong because imputing with mean would assign incorrect values to new customers. Option C is wrong because using a model to predict missing values is overkill and may introduce error when the true value is known to be 0.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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