The answer is that SageMaker Data Wrangler can export features to SageMaker Feature Store, and it can also export the entire data flow as a SageMaker Processing script. This is correct because Data Wrangler is designed not just for interactive visual feature engineering, but for operationalizing that work; the export to Feature Store allows you to store, discover, and share curated features for reuse across models, while the Processing script export lets you automate the entire transformation pipeline at scale on managed infrastructure. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding that Data Wrangler bridges ad‑hoc exploration and production pipelines—a common trap is thinking Data Wrangler only works within SageMaker Studio and cannot export reusable artifacts. Remember the memory tip: "Wrangler wrangles, then exports to Feature Store or Processing script—never leaves you stranded in the notebook."
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 using SageMaker Data Wrangler to prepare features for a classification model. Which TWO statements about feature engineering in Data Wrangler are correct?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Transformations created in Data Wrangler can be exported as a SageMaker Processing script
Option C is correct because SageMaker Data Wrangler allows you to export the entire data flow, including all transformations, as a SageMaker Processing script. This script can be run at scale on managed infrastructure, enabling you to operationalize the feature engineering pipeline for training or inference without manual rework.
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.
✗
Data Wrangler only supports CSV and Parquet input formats
Why it's wrong here
Data Wrangler supports CSV, Parquet, JSON, and more.
✗
Data Wrangler enables writing custom PySpark transformations
Why it's wrong here
Data Wrangler uses its own transforms, not custom PySpark.
✓
Transformations created in Data Wrangler can be exported as a SageMaker Processing script
Why this is correct
Data Wrangler can generate a processing script for reuse.
Related concept
Read the scenario before looking for a memorised answer.
✗
Data Wrangler automatically scales features for XGBoost models
Why it's wrong here
Scaling is not automatic and not required for tree-based models.
✓
Data Wrangler can export features to SageMaker Feature Store
Why this is correct
Features can be written directly to Feature Store.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume Data Wrangler supports custom PySpark transformations (Option B) because it integrates with Spark, but in reality, custom code must be written outside the visual interface, and only built-in transforms are available within Data Wrangler itself.
Detailed technical explanation
How to think about this question
Under the hood, Data Wrangler generates a .flow file that captures the transformation DAG, and when exporting to a Processing script, it converts that DAG into a Python script using the sagemaker_datawrangler library. This script can be parameterized and run as a SageMaker Processing job, which spins up a managed container with the necessary dependencies, allowing you to process large datasets that exceed the memory limits of the Data Wrangler UI. A real-world scenario is when you need to apply the same feature engineering steps to a production dataset of terabytes, where the Processing script ensures reproducibility and scalability.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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 — 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: Transformations created in Data Wrangler can be exported as a SageMaker Processing script — Option C is correct because SageMaker Data Wrangler allows you to export the entire data flow, including all transformations, as a SageMaker Processing script. This script can be run at scale on managed infrastructure, enabling you to operationalize the feature engineering pipeline for training or inference without manual rework.
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. In SageMaker Data Wrangler, you have a flow that imports data from Amazon S3 and needs to join it with a table from Amazon Redshift. The data volumes are large (hundreds of GB). Which approach is most efficient within Data Wrangler?
hard
A.Use Amazon Athena federated query to join in place and import the result
B.Export the Redshift table to S3 as Parquet, then import both datasets into Data Wrangler and join
C.Use AWS Glue to join the datasets and output to S3, then import the joined result into Data Wrangler
✓ D.Import the Redshift table directly using a Data Wrangler source step and apply a join transform
Why D: Option D is correct because SageMaker Data Wrangler natively supports Amazon Redshift as a source via a direct connection, allowing you to import the Redshift table as a source step and then apply a join transform within the same visual flow. This approach avoids unnecessary data movement or intermediate exports, which is critical for hundreds of GB of data, as it leverages Data Wrangler's optimized in-memory and Spark-based processing to perform the join efficiently.
Variation 2. A data scientist is using SageMaker Data Wrangler to prepare a large dataset. The data contains duplicate rows, which could bias the model. Which built-in step in Data Wrangler can automatically detect and remove duplicates?
medium
A.Amazon QuickSight duplicate detection
✓ B.Handle Duplicates transform in Data Wrangler
C.AWS Glue Studio FindDuplicates transform
D.Amazon DataZone catalog
Why B: The Handle Duplicates transform is a built-in step in SageMaker Data Wrangler specifically designed to detect and remove duplicate rows from a dataset. It provides configurable options such as selecting a subset of columns for duplicate detection and choosing whether to keep the first or last occurrence, directly addressing the bias risk from duplicate rows in ML training data.
Variation 3. A data scientist is preparing a large dataset for training a machine learning model. The dataset contains missing values in several columns. Which approach is the MOST efficient for handling missing values in a large dataset using AWS services?
easy
A.Use AWS Glue ETL to write a custom Python script that imputes missing values with the mean.
✓ B.Use Amazon SageMaker Data Wrangler to impute missing values using built-in transforms.
C.Use pandas in a SageMaker notebook to impute missing values with the median.
D.Remove all rows with missing values from the dataset.
Why B: Amazon SageMaker Data Wrangler provides a visual interface and built-in transforms for handling missing values efficiently at scale, without writing custom code. Glue ETL is more code-heavy, and imputation with pandas is not scalable for large datasets. Removing all rows with missing values is not always optimal and may not be efficient.
Last reviewed: Jun 24, 2026
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