20+ practice questions focused on Data Preparation for Machine Learning — one of the most tested topics on the AWS Certified Machine Learning Engineer Associate MLA-C01 exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Data Preparation for Machine Learning PracticeA 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?
Explanation: 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 DynamicFrame's `repartition()` method allows you to control the number of output files, and writing as Parquet directly from Glue avoids intermediate conversions, making it the most efficient choice for this task.
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?
Explanation: 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 particularly effective for tabular data in Amazon SageMaker, as it increases the representation of the minority class without simply duplicating existing samples, thereby reducing overfitting and improving the model's ability to learn decision boundaries for the minority class.
A company uses AWS Glue to run ETL jobs that prepare data for machine learning. The data is stored in Amazon S3 in Parquet format. A data engineer notices that the Glue job is running slowly and consuming a lot of resources. What is the MOST cost-effective way to improve the performance of the Glue job?
Explanation: Increasing the number of DPUs (Data Processing Units) in AWS Glue can improve parallelism and reduce job runtime, but it increases cost. Using G.1X worker type with more memory per worker can improve performance without increasing DPU count, offering better resource utilization. Switching to CSV may degrade performance. Using partition pruning on the source data can reduce data scanned but may not address resource consumption.
A machine learning team is building a model using a dataset that contains a mix of numerical and categorical features. The categorical features have high cardinality (e.g., zip code with thousands of unique values). The team wants to use Amazon SageMaker for training. Which technique should the team use to encode the high-cardinality categorical features effectively?
Explanation: Target encoding replaces each high-cardinality category with a statistic (typically the mean of the target) computed from the training data, which compresses thousands of zip codes into a single informative numeric feature without exploding dimensionality. It is the standard SageMaker-recommended approach for high-cardinality categoricals because it preserves predictive signal while keeping the feature space small.
A machine learning team is preparing a dataset for a regression model. The dataset contains numerical features that are on different scales (e.g., age 0-100, income 0-1,000,000). The team plans to use Amazon SageMaker to train a linear regression model. Which THREE data preparation steps should the team take to ensure the model performs well? (Select THREE.)
Explanation: Option C is correct because missing values in a dataset will cause errors or bias in SageMaker linear regression training, so imputing them (e.g., mean/median) or removing affected rows ensures a complete numeric matrix for the algorithm. Option D is correct because linear regression in SageMaker requires all input features to be numeric, so any categorical variables must be converted via one-hot encoding (or similar numeric encoding) before training. Option E is correct because the scenario explicitly states features are on very different scales (age 0-100 vs. income 0-1,000,000); standardization (z-score) or min-max normalization puts them on comparable scales, which improves convergence and coefficient stability for linear regression. Option A is not required: feature selection can help reduce overfitting or dimensionality, but it is not a mandatory preparation step to make linear regression perform well with differently scaled numeric features. Option B is not required: outlier removal can be beneficial in some cases, but it is not a necessary data preparation step for the stated goal and may discard valid data; the question asks for steps the team should take to ensure good performance, and scaling, missing-value handling, and numeric encoding are the essential ones.
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Practice all Data Preparation for Machine Learning questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Data Preparation for Machine Learning. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Data Preparation for Machine Learning questions on the MLA-C01 frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Data Preparation for Machine Learning is tested as part of the AWS Certified Machine Learning Engineer Associate MLA-C01 blueprint. Practicing with targeted Data Preparation for Machine Learning questions ensures you can handle any format or difficulty that appears.
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