Free · No account needed · No credit card

AWS Certified Machine Learning Engineer Associate MLA-C01 Practice Test

507 questions with instant explanations, domain breakdown, and wrong-answer analysis. Built for the real exam.

Instant feedback after each answer
Full explanations included
Domain score breakdown
Real exam: 130 min
Pass mark: 700%

Sample questions with explanations

This is exactly what you see during practice — question, options, and a full explanation after you answer.

Q1Data Preparation for Machine Learningeasy
Full explanation →

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?

AUse AWS Glue ETL to write a custom Python script that imputes missing values with the mean.
Use Amazon SageMaker Data Wrangler to impute missing values using built-in transforms.Correct
CUse pandas in a SageMaker notebook to impute missing values with the median.
DRemove all rows with missing values from the dataset.

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 mis…Read full explanation

Q2Data Preparation for Machine Learningmedium
Full explanation →

A company is using AWS Glue to prepare data for a machine learning pipeline. The source data is in an Amazon S3 bucket in CSV format. The data scientist wants to convert the data to Parquet format and partition it by date. Which AWS Glue feature should be used to optimize the data for query performance and reduce storage costs?

AUse Amazon Athena to convert the data to JSON format and store it in S3.
Use AWS Glue DynamicFrame to repartition the data and write it as Parquet.Correct
CUse AWS Glue to convert the data to Apache Hive format.
DUse Apache Spark DataFrame to write the data as CSV with Snappy compression.

Option B is correct because AWS Glue DynamicFrames provide built-in optimizations for writing data in columnar formats like Parquet, which improves query performance through predicate pushdown and compression, and reduces storage costs by using efficient encoding. The DynamicFram…Read full explanation

Q3Data Preparation for Machine Learninghard
Full explanation →

A machine learning engineer is preparing a dataset for a binary classification model. The dataset has a severe class imbalance (95% class A, 5% class B). The engineer wants to use Amazon SageMaker to train the model. Which data preparation technique should the engineer apply to the training dataset to address the imbalance and improve model performance?

AApply data augmentation to the majority class by adding noise.
Apply Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic samples for the minority class.Correct
CUse a weighted loss function during training to penalize misclassifications of the minority class.
DApply random under-sampling to reduce the majority class to match the minority class size.

Option B is correct because SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances, which directly addresses the severe class imbalance (95% class A, 5% class B) by creating a more balanced training dataset. This technique is…Read full explanation

Untimed Practice

Answer at your own pace. Explanation and domain tag shown immediately after each answer.

Timed Practice

Countdown timer starts immediately. Results and domain scores shown at the end — just like the real exam.

Why practice here?

Full explanations on every question

Not just the right answer — you get exactly why each wrong option is wrong, so you learn the concept, not the answer.

Domain score breakdown

After each session see your score by exam domain so you know exactly where to focus study time.

100% free, forever

No subscription, no trial, no email wall. Start a session in under 10 seconds.

Exam-style questions

Scenario-based, precise wording, realistic distractors — written to match what you actually see on exam day.

← All MLA-C01 questionsMLA-C01 exam guideStudy guidePractice by domain