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 SageMaker Processing job fails with the error: 'Unable to parse CSV file due to inconsistent number of columns'. The data is stored as CSV in S3. What is the most likely cause?
Explanation: The 'inconsistent number of columns' error in a SageMaker Processing job directly indicates that some rows in the CSV file have fewer fields than expected. SageMaker's built-in CSV parser expects a uniform number of columns per row; missing values (e.g., trailing commas omitted or blank fields not represented) cause row lengths to differ, triggering this specific parsing failure.
A company has 10 TB of log data in compressed JSON format stored in Amazon S3. The data needs to be processed and transformed into a structured format for machine learning. The processing requires complex transformations, including parsing nested JSON and joining with a reference table. The company wants to minimize infrastructure management. Which approach should the company use?
Explanation: AWS Glue ETL with PySpark (Option D) is the best choice because it provides a fully serverless environment, minimizing infrastructure management. Glue can handle complex transformations like parsing nested JSON and joining with reference tables using PySpark, and it scales automatically for large datasets (10 TB). Amazon EMR (Option C) requires cluster management and provisioning, which contradicts the goal of minimizing management overhead.
A company needs to anonymize personally identifiable information (PII) in a dataset before using it for ML. The dataset is stored in S3 as CSV files. The team wants to mask credit card numbers by replacing all digits except the last four with asterisks. Which approach is the most scalable?
Explanation: The most scalable because AWS Glue with PySpark UDFs can process large datasets (e.g., 500 GB) in a distributed manner across multiple nodes, handling the masking transformation efficiently. Option A (Amazon Comprehend) adds unnecessary cost and latency for simple regex-based masking, and Option B (Python shell job) runs on a single node and does not scale to handle 500 GB effectively.
Refer to the exhibit. A data engineer runs a Glue ETL job that uses a Python script. The job fails because of a missing module `scikit-learn`. Which fix is MOST appropriate?
Explanation: AWS Glue jobs (both ETL and Python shell) support the --additional-python-modules job parameter, which allows you to specify extra Python packages to install. This is the standard way to add libraries like scikit-learn that are not pre-installed. It avoids the need to install at runtime (Option A) or switch job types unnecessarily (Option D). Option C switching to Spark would add complexity and not directly solve the missing module.
A data scientist needs to prepare a dataset for a binary classification model. The dataset contains 100,000 records with 50 features, including categorical variables with high cardinality, missing values in 30% of records for a key numeric feature, and a severe class imbalance (5% positive class). The data is stored in an Amazon S3 bucket. Which TWO actions should the data scientist take to improve model performance and ensure robust data preparation? (Choose two.)
Explanation: Stratified sampling preserves the class imbalance ratio (5% positive class) in both training and test sets, ensuring the model is evaluated on a representative distribution and avoiding overfitting to the majority class in training. Option E is correct because standardizing numeric features with scikit-learn's StandardScaler in an AWS Glue job ensures that features have zero mean and unit variance, which is critical for distance-based algorithms (e.g., SVM, k-NN) and many neural networks. Option B is incorrect because deleting records with missing values would discard 30% of data, leading to significant information loss and potential bias, and is especially harmful when missingness is non-random. Option C is incorrect because one-hot encoding high-cardinality categorical features creates a very large number of dummy variables, causing the curse of dimensionality and making the model computationally expensive and prone to overfitting. Option D is incorrect because random undersampling of the majority class discards potentially valuable data, often reducing model performance; techniques like SMOTE or class weights are preferred to address class imbalance.
+15 more Data Preparation for Machine Learning questions available
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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