- A
Use AWS Glue ETL to impute missing lab results with a value predicted from other features using a model like XGBoost, and apply count encoding to diagnosis codes based on their frequency of occurrence.
Predictive imputation leverages other features to estimate missing values, retaining data. Count encoding reduces the cardinality of diagnosis codes.
- B
Replace missing lab results with the overall mean, and use a binary flag for nullness. For diagnosis codes, apply one-hot encoding after grouping codes into 20 categories based on clinical relevance.
Why wrong: Mean imputation is simple but may distort distribution; the null flag helps but predictive imputation is better. Grouping diagnoses into 20 categories is a good idea, but one-hot encoding those 20 would be simpler than count encoding? Actually count encoding is better for 10k categories. This option's group-and-encode is partially valid, but the imputation is not optimal.
- C
Drop all records where lab_result is null, and use one-hot encoding for diagnosis codes.
Why wrong: Dropping 60% of records reduces the dataset size greatly and can introduce selection bias. One-hot encoding 10,000 codes is not feasible.
- D
Use Amazon SageMaker Data Wrangler's built-in 'Fill missing' with KNN imputation for lab results, and apply ordinal encoding to diagnosis codes based on the order of ICD-10 chapters.
Why wrong: KNN imputation on 60% missing data is computationally intensive and assumes local similarity; ordinal encoding by chapter may not be meaningful for model performance.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 healthcare company is developing a predictive model to identify patients at risk of readmission within 30 days after discharge. The dataset contains electronic health record (EHR) data from multiple hospitals, stored as Parquet files in Amazon S3. The data includes patient demographics, diagnoses (ICD-10 codes), medications, lab results, and length of stay. A data scientist notices that the 'lab_result' column has a high number of null values (over 60%) because some tests are not applicable to all patients. Additionally, the 'diagnosis_code' column has over 10,000 unique ICD-10 codes. The company wants to build a model that complies with HIPAA and performs well. The data scientist must prepare the features efficiently using AWS services. Which combination of steps should the data scientist take? (Assume the company can use any AWS service.)
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
Use AWS Glue ETL to impute missing lab results with a value predicted from other features using a model like XGBoost, and apply count encoding to diagnosis codes based on their frequency of occurrence.
Option A is correct because it uses AWS Glue ETL to impute missing lab results with a predictive model (XGBoost), which is appropriate for high missingness (>60%) where simple imputation would bias the model, and applies count encoding to the high-cardinality diagnosis codes (10,000+ unique values) to avoid the dimensionality explosion of one-hot encoding while preserving frequency information. This approach balances HIPAA compliance (data stays within AWS) with model performance.
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 AWS Glue ETL to impute missing lab results with a value predicted from other features using a model like XGBoost, and apply count encoding to diagnosis codes based on their frequency of occurrence.
Why this is correct
Predictive imputation leverages other features to estimate missing values, retaining data. Count encoding reduces the cardinality of diagnosis codes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Replace missing lab results with the overall mean, and use a binary flag for nullness. For diagnosis codes, apply one-hot encoding after grouping codes into 20 categories based on clinical relevance.
Why it's wrong here
Mean imputation is simple but may distort distribution; the null flag helps but predictive imputation is better. Grouping diagnoses into 20 categories is a good idea, but one-hot encoding those 20 would be simpler than count encoding? Actually count encoding is better for 10k categories. This option's group-and-encode is partially valid, but the imputation is not optimal.
- ✗
Drop all records where lab_result is null, and use one-hot encoding for diagnosis codes.
Why it's wrong here
Dropping 60% of records reduces the dataset size greatly and can introduce selection bias. One-hot encoding 10,000 codes is not feasible.
- ✗
Use Amazon SageMaker Data Wrangler's built-in 'Fill missing' with KNN imputation for lab results, and apply ordinal encoding to diagnosis codes based on the order of ICD-10 chapters.
Why it's wrong here
KNN imputation on 60% missing data is computationally intensive and assumes local similarity; ordinal encoding by chapter may not be meaningful for model performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose simple mean imputation (Option B) or dropping rows (Option C) without considering the impact of high missingness on bias and data loss, or they overcomplicate encoding (Option D) without recognizing that ordinal encoding implies a false order for categorical codes.
Trap categories for this question
Similar concept trap
KNN imputation on 60% missing data is computationally intensive and assumes local similarity; ordinal encoding by chapter may not be meaningful for model performance.
Detailed technical explanation
How to think about this question
Count encoding (also called frequency encoding) replaces each category with its frequency count, which is a form of target-agnostic encoding that works well for high-cardinality categorical features in tree-based models like XGBoost, as it captures popularity without overfitting. AWS Glue ETL can run custom Python transforms (e.g., using Spark MLlib or XGBoost4J) to impute missing values, and the Parquet format in S3 supports columnar pruning and compression, making it efficient for large EHR datasets. Under HIPAA, all data processing should occur within AWS's HIPAA-eligible services (Glue, SageMaker) with encryption at rest and in transit.
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 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 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
507 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.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
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.
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: Use AWS Glue ETL to impute missing lab results with a value predicted from other features using a model like XGBoost, and apply count encoding to diagnosis codes based on their frequency of occurrence. — Option A is correct because it uses AWS Glue ETL to impute missing lab results with a predictive model (XGBoost), which is appropriate for high missingness (>60%) where simple imputation would bias the model, and applies count encoding to the high-cardinality diagnosis codes (10,000+ unique values) to avoid the dimensionality explosion of one-hot encoding while preserving frequency information. This approach balances HIPAA compliance (data stays within AWS) with model performance.
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 →
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.