MLA-C01 Data Preparation for Machine Learning Practice Test 4 — 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?

Accepted Answer

Stratified split. Option C is correct because 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.

Answer

K-fold cross-validation split

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Chronological split

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Random split

Question 2

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?

Accepted Answer

Import the Redshift table directly using a Data Wrangler source step and apply a join transform. 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.

Answer

Use Amazon Athena federated query to join in place and import the result

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Export the Redshift table to S3 as Parquet, then import both datasets into Data Wrangler and join

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Use AWS Glue to join the datasets and output to S3, then import the joined result into Data Wrangler

Question 3

A dataset for binary classification has a severe class imbalance (5% positive class). Which two data preparation techniques can help address this imbalance? (Choose two.)

Accepted Answer

Undersample the majority class. Option D is correct because undersampling the majority class reduces the number of instances from the dominant class, helping to balance the dataset and prevent the model from being biased toward the majority class. This technique is straightforward and can be effective when the majority class has redundant or noisy samples, though it risks losing valuable information.

Answer

Remove outliers from the minority class

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Apply PCA to reduce dimensionality

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Use stratified splitting for train/test sets

Question 4

You are preparing a time-series dataset for a forecasting model. Which three steps are critical to prevent data leakage during preprocessing? (Choose three.)

Accepted Answer

Standardize features using parameters computed only from the training set. Standardizing features using parameters computed only from the training set is critical because it prevents information from the test set from influencing the training data. If you compute the mean and standard deviation from the entire dataset before splitting, the test set's distribution leaks into the training process, causing the model to see future data during training. This violates the temporal order and leads to overly optimistic performance estimates.

Answer

Impute missing values using the mean of the entire dataset

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Shuffle the data randomly before splitting

Question 5

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. AWS Glue can perform incremental processing by using job bookmarks to track previously processed data and only process new partitions or files. This avoids reprocessing the entire historical dataset, making it efficient for updating ML training datasets with daily log data.

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 6

Refer to the exhibit. A SageMaker Processing job configured as above fails with a timeout error. The input data is 100 GB of CSV files. The processing script performs standard data cleaning operations. What is the most likely cause?

Accepted Answer

The processing job does not have enough memory for the data volume. Option A is correct because the SageMaker Processing job is configured with a single `ml.m5.large` instance, which has 8 GiB of memory. The input data is 100 GB of CSV files, and the processing script performs standard data cleaning operations that typically load the entire dataset into memory (e.g., using pandas). With only 8 GiB of RAM, the instance cannot hold 100 GB of data, causing the job to run out of memory and eventually fail with a timeout error as the OS kills the process or the job hangs.

Answer

The container entrypoint is missing the full path to the script

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The S3Input S3CompressionType is set to "None" but the file is compressed

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The IAM role does not have permission to write to the output bucket

Question 7

Refer to the exhibit. A Glue job runs successfully the first time but on subsequent runs with new data (added to the same input location), the job does not process the new data. What is the most likely cause?

Accepted Answer

The job bookmark is enabled, causing the job to skip already processed data. Option C is correct because when a Glue job bookmark is enabled, the job tracks previously processed data using a persistent state stored in a DynamoDB table. On subsequent runs, the bookmark mechanism skips files that have already been processed, so new data added to the same input location is ignored unless the bookmark is reset or the job is configured to process new partitions. This explains why the first run succeeds but later runs do not process new data.

Answer

The script location is incorrect

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The MaxRetries is set to 0, so the job does not retry on failure

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The WorkerType is Standard, which does not support incremental processing

Question 8

Refer to the exhibit. The Glue job reads a CSV file and attempts to write to a Parquet table. What is the most likely cause of this error?

Accepted Answer

The 'price' column contains non-numeric values in some rows. Option C is correct because the error message indicates a 'NumberFormatException' when parsing the 'price' column, which occurs when Spark attempts to convert a string value to a numeric type. Since the Glue job's schema inference likely detected 'price' as a numeric column based on the majority of rows, any row containing a non-numeric value (e.g., 'N/A', 'null', or a currency symbol) will cause this parsing failure during the write to Parquet.

Answer

The 'price' column is missing from some rows

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The schema inference incorrectly detected the column as String

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The CSV file is compressed and not properly decompressed

Question 9

A company uses SageMaker Processing jobs to clean customer transaction data. The processing script runs on a single ml.m5.large instance and takes 30 minutes to process 50 GB of data in CSV format. To reduce processing time, the company wants to process 200 GB of data within 1 hour. Which combination of changes should the company make?

Accepted Answer

Increase InstanceCount to 4 and convert the data to Parquet format.. Option C is correct because increasing InstanceCount to 4 allows parallel processing of the 200 GB dataset across multiple ml.m5.large instances, each handling 50 GB, which directly reduces processing time. Converting the data from CSV to Parquet format further accelerates processing by enabling columnar storage and predicate pushdown, reducing I/O and CPU overhead. Together, these changes can achieve the goal of processing 200 GB within 1 hour, as the original 50 GB took 30 minutes on a single instance.

Answer

Run the job in local mode with a larger EBS volume.

Answer

Increase VolumeSizeInGB to 100 and use gzip compression.

Answer

Use a larger instance type (e.g., ml.r5.4xlarge) and keep the same script.

Question 10

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.

Answer

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 11

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 12

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 13

A data scientist is preprocessing time series data for a fraud detection model. The data includes transaction timestamps, amounts, and merchant IDs. The model should predict fraud within seconds of a transaction. The data scientist wants to avoid data leakage by not using future information to predict past events. Which data preparation practice should be implemented?

Accepted Answer

Compute features like lagged transaction amounts and rolling statistics based only on each transaction's past data up to that point.. Option A is correct because it ensures that features are computed using only historical data available up to each transaction's timestamp, preventing any future information from leaking into the model. In time series fraud detection, using only past data for lagged amounts and rolling statistics respects the temporal order and avoids the model learning patterns that would not be available at prediction time.

Answer

Randomly shuffle the dataset before splitting into training and validation sets.

Answer

Generate features such as rolling averages and lag features using a sliding window of all available data.

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Normalize the features using MinMaxScaler on the entire dataset before splitting into training and testing.

Question 14

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 Amazon EMR with Apache Spark.. Option C is correct because Amazon EMR with Apache Spark is designed for large-scale data processing (10 TB) and can handle complex transformations like parsing nested JSON and joining with reference tables efficiently. Spark's in-memory processing and support for structured data via DataFrames make it ideal for this workload, while EMR minimizes infrastructure management by providing a managed Hadoop/Spark cluster.

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 AWS Glue ETL with PySpark.

Question 15

A team is collaborating on a machine learning project and needs to ensure that data used for training is consistent across experiments. The team wants to version datasets, track data lineage, and be able to reproduce past experiments. The team uses SageMaker for model training. Which combination of services and features should the team use?

Accepted Answer

Use SageMaker with AWS Lake Formation to manage data access, version datasets in S3, and use SageMaker Experiments to track training jobs.. Option C is correct because it combines AWS Lake Formation for fine-grained data access control and governance, S3 versioning for dataset versioning, and SageMaker Experiments to track training jobs and lineage. This trio directly addresses the need for consistent data across experiments, versioning, lineage tracking, and reproducibility in SageMaker.

Answer

Use SageMaker Pipelines to automate training and store datasets in S3 with versioning enabled.

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Store datasets in Amazon DynamoDB and use Amazon Athena to query specific versions.

Answer

Use S3 versioning to store all dataset versions and AWS Glue Data Catalog to track schema changes.