MLA-C01 Data Preparation for Machine Learning Practice Test 9 — 15 Questions

Question 1

A data scientist needs to split a dataset into training, validation, and test sets. The dataset has a categorical target variable with imbalanced class distribution. Which splitting technique ensures that each subset has a similar proportion of each class?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Stratified split. Stratified splitting preserves the original class proportions in each subset (training, validation, test) by sampling each class independently. This is critical for imbalanced datasets to avoid skewed distributions that could bias model evaluation or training.

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K-fold cross-validation split

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Chronological split

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Random split

Question 2

A company ingests daily log data into an S3 bucket. They need to update the existing ML training dataset with new data without reprocessing the entire history. Which two strategies should they adopt? (Choose two.)

Accepted Answer

Use AWS Glue to incrementally process new partitions. Option B is correct because AWS Glue can perform incremental processing by reading only newly added partitions (using job bookmarks and partition predicates) rather than reprocessing the full history, which directly satisfies the requirement to update the dataset without re-running the entire pipeline. Option C is correct because partitioning the S3 data by a key such as date lets new daily log data land in a new partition, so downstream training jobs or Glue ETL can scan only the new partition and append it to the existing dataset. Option A is incorrect because S3 objects are immutable and cannot be appended to in place; a single large file would require rewriting the whole object. Option D is incorrect because manually copying files into the same bucket does not provide incremental processing logic or partition awareness, and is error-prone. Option E is incorrect because overwriting the entire dataset forces full reprocessing, which is exactly what the scenario wants to avoid.

Answer

Store all data in a single large file and use append operations

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Manually copy new files to the same S3 bucket

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Overwrite the entire existing dataset with the new data

Question 3

A data scientist is training a binary classifier on a highly imbalanced dataset (1:100 class ratio). The dataset contains 500,000 rows and 30 features. The data is stored in S3 in Parquet format. The data scientist wants to use SageMaker's built-in XGBoost algorithm. Which data preparation technique should the data scientist apply to best address the class imbalance without causing data leakage?

Accepted Answer

Use the scale_pos_weight parameter in XGBoost to assign higher weight to the minority class.. The scale_pos_weight parameter in XGBoost directly adjusts the loss function to penalize misclassifications of the minority class more heavily, effectively handling class imbalance without modifying the dataset. This avoids data leakage because the weighting is applied during training only, not during preprocessing, and does not involve any synthetic data generation or resampling that could inadvertently expose test information.

Answer

Undersample the majority class to create a balanced dataset, then split.

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Oversample the minority class using SMOTE on the entire dataset before splitting into train/validation sets.

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Randomly oversample the minority class by duplicating rows, then perform stratified train/test split.

Question 4

A data engineer needs to prepare a large dataset for machine learning. The data is stored in an Amazon RDS MySQL database and needs to be transformed and moved to an S3 bucket in Parquet format for use with SageMaker. Which AWS service is most suitable for this extraction, transformation, and loading (ETL) task?

Accepted Answer

Use AWS Glue ETL jobs with PySpark to read from RDS, apply transformations, and write to S3 as Parquet.. AWS Glue ETL jobs with PySpark are the most suitable service for this task because Glue is a fully managed, serverless ETL service that can natively connect to Amazon RDS MySQL via JDBC, apply transformations using PySpark, and write the output directly to S3 in Parquet format. This aligns perfectly with the requirement to extract, transform, and load a large dataset into a machine-learning-ready format without managing infrastructure.

Answer

Use Amazon Athena CTAS statements to copy data from RDS to S3.

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Use SageMaker Data Wrangler to connect to RDS and export transformed data to S3.

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Use Amazon EMR with Spark to read from RDS, transform, and write to S3.

Question 5

A team is building a recommendation system and wants to store and serve features for online and offline models. The features include user statistics (updated daily) and movie metadata (static). The team needs low-latency inference for real-time recommendations and wants to reuse features across multiple models. Which AWS service should the team use to store, manage, and serve these features?

Accepted Answer

SageMaker Feature Store.. Amazon SageMaker Feature Store is purpose-built for storing, managing, and serving ML features with low-latency retrieval for online inference and batch serving for offline training. It supports feature reuse across multiple models by providing a centralized feature registry, consistent feature definitions, and both online (low-latency) and offline (S3-based) stores, which directly matches the team's requirements for real-time recommendations and cross-model reuse.

Answer

Amazon DynamoDB with TTL.

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AWS Glue Data Catalog.

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Amazon S3 with AWS Lambda for serving.

Question 6

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?

Accepted Answer

Use AWS Glue ETL with PySpark.. 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.

Answer

Use SageMaker Processing jobs to run custom scripts.

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Use Amazon Athena to query and transform the data.

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Use Amazon EMR with Apache Spark.

Question 7

A company has a dataset of 2 billion records stored as text files in Amazon S3. The data is partitioned by year and month. The data science team wants to read only the last 6 months of data for model training using SageMaker. To minimize data scanned and reduce costs, which approach should the team use?

Accepted Answer

Use AWS Glue to create a catalog table with partitions, then query with Athena to create a filtered dataset in S3.. AWS Glue can crawl the S3 data to create a catalog table with partitions by year and month. Athena can then query only the partitions corresponding to the last 6 months, scanning minimal data and writing the filtered results back to S3 for SageMaker training. This approach leverages partition pruning to reduce costs and avoids loading or processing the full 2 billion records.

Answer

Use S3 Select to retrieve only the last 6 months of data by applying an SQL expression on each object.

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Use SageMaker Processing with a script that lists all objects in the bucket and reads only those with the desired prefixes.

Answer

Use SageMaker Processing with Input Mode 'File' and specify the S3 prefix for the last 6 months.

Question 8

A company is building a fraud detection model on an imbalanced dataset (99% legitimate, 1% fraudulent). To improve recall on the minority class, they want to resample data. Which combination of techniques should they use?

Accepted Answer

SMOTE on training set only. SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic minority-class samples by interpolating between existing minority instances. Applying SMOTE only to the training set prevents synthetic samples from leaking into the test set, which would inflate evaluation metrics and produce an overly optimistic model. This is the correct resampling approach for improving recall on an imbalanced fraud dataset.

Answer

SMOTE on entire dataset before train/test split

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Random oversampling of minority class before train/test split

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Random undersampling of majority class

Question 9

A team is using Amazon SageMaker for feature engineering. They have a dataset with a column 'TransactionDate' in string format (e.g., '2023-01-15 10:30:00'). They need to create features: year, month, day, hour, and day_of_week. What is the most efficient way to do this in a SageMaker processing job?

Accepted Answer

Use pandas datetime functions and then split. Using pandas datetime functions within a SageMaker processing job is the most efficient approach for this task. SageMaker processing jobs run custom Python scripts, and pandas provides vectorized operations (e.g., `pd.to_datetime()`, `.dt.year`, `.dt.month`, `.dt.day`, `.dt.hour`, `.dt.dayofweek`) that parse the string column and extract all required features in a single pass without external dependencies or data movement.

Answer

Use SageMaker built-in first party algorithms

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Use AWS Glue for transformation

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Use SQL query in Athena on S3 data

Question 10

An ML engineer needs to split a dataset into training, validation, and test sets. The dataset has a time-based column that should not be leaked. Which split method is most appropriate?

Accepted Answer

Temporal split based on date. A temporal split ensures that the time-based column is not leaked by preserving the chronological order of the data. This method uses the date column to assign earlier records to the training set and later records to the validation and test sets, preventing future information from influencing the model during training.

Answer

Stratified split based on target

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Random split with 70/20/10

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K-fold cross-validation

Question 11

A company collects sensor data from IoT devices. The data arrives with missing timestamps due to network issues. For anomaly detection, the engineer needs to create features that capture rolling statistics over fixed windows. Which data preprocessing step is essential before feature generation?

Accepted Answer

Resample data to a fixed frequency. Resampling the data to a fixed frequency is essential because rolling window statistics require a consistent time index to compute accurate aggregations over fixed windows. Without a uniform timestamp grid, the window boundaries become ambiguous and the resulting features will be misaligned or incomplete, undermining the anomaly detection model.

Answer

Remove missing timestamps

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Sort data by device ID

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Impute missing values with forward fill

Question 12

A machine learning engineer is preparing a dataset for a binary classification model. The dataset has 10,000 rows and 200 features, with 5% positive class. The engineer suspects class imbalance may affect model performance. Which TWO actions should the engineer take to mitigate imbalance? (Choose 2.)

Accepted Answer

Apply SMOTE only to training data. Option D is correct because SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples of the minority class and must be applied only to the training data to avoid data leakage into validation/test folds, directly addressing the 5% positive-class imbalance. Option E is correct because setting class weights in the algorithm (e.g., class_weight='balanced' in scikit-learn) penalizes misclassification of the minority class more heavily, which mitigates imbalance without altering the dataset. Option A is incorrect because PCA is a dimensionality-reduction technique for the 200 features and does nothing to change the 5% class ratio. Option B is incorrect because removing low-variance features is a feature-selection step unrelated to class distribution. Option C is incorrect because k-fold cross-validation is an evaluation/resampling strategy that provides more reliable performance estimates but does not itself rebalance classes.

Answer

Perform PCA to reduce dimensions

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Remove features with low variance

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Use k-fold cross-validation

Question 13

A data engineer needs to convert a JSON dataset to Parquet format for efficient querying with Amazon Athena. The JSON files are in an S3 bucket. Which service can perform this conversion with minimal coding?

Accepted Answer

AWS Glue Studio with a visual job. AWS Glue Studio with a visual job is the correct choice because it provides a no-code, drag-and-drop interface to create ETL jobs that can read JSON from S3 and write it as Parquet, with built-in schema inference and transformation capabilities. This minimizes coding effort while leveraging Glue's serverless Spark engine for efficient conversion, making it ideal for preparing data for Athena queries.

Answer

Amazon SageMaker Processing

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Amazon EMR

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AWS Lambda

Question 14

A team is using AWS Glue to process streaming data from Amazon Kinesis. The streaming data contains both structured and semi-structured fields. The team needs to flatten the semi-structured fields into columns for downstream ML training. Which Glue feature is BEST suited?

Accepted Answer

Relationalize transform. The Relationalize transform is specifically designed to flatten nested JSON or semi-structured fields into a relational structure, making it ideal for converting complex streaming data from Kinesis into flat columns for ML training. It automatically handles arrays and structs by creating separate tables or columns, which is exactly what the team needs for downstream processing.

Answer

Spigot transform

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ResolveChoice transform

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ApplyMapping transform

Question 15

Refer to the exhibit. A data scientist is trying to use AWS Glue to read data from the S3 bucket `ml-data-bucket`. The Glue job fails with an access denied error. What is the most likely cause?

Accepted Answer

The policy does not include s3:ListBucket permission on the bucket. The error occurs because the IAM policy attached to the Glue job role grants s3:GetObject on the bucket objects (via the `arn:aws:s3:::ml-data-bucket/*` resource) but does not include the s3:ListBucket permission on the bucket itself (`arn:aws:s3:::ml-data-bucket`). When AWS Glue reads data from S3, it first performs a ListBucket operation to enumerate objects in the bucket or prefix, and without that permission, the request is denied even if GetObject is allowed.

Answer

The policy allows s3:PutObject but the job only reads

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The policy does not specify the bucket ARN without /*

Answer

The Glue job role does not have the required permissions