MLA-C01 Data Preparation for Machine Learning Practice Test 7 — 15 Questions

Question 1

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?

Accepted Answer

Use a Glue Python shell job instead. Option D is correct because a Glue Python shell job includes pre-installed libraries like scikit-learn, eliminating the missing module error without additional configuration. This job type is designed for lightweight Python scripts that do not require the distributed processing of Spark, making it the most appropriate fix for a simple dependency issue.

Answer

Modify the script to install scikit-learn using pip at runtime

Answer

Add a --additional-python-modules argument to the job with scikit-learn

Answer

Switch to a Glue job using Spark instead of Python

Question 2

Refer to the exhibit. A data engineer deploys this Glue job via CloudFormation. When running, the job fails with a timeout after 2 hours. The job processes a large dataset and expected to take 3 hours. Which change would resolve the issue?

Accepted Answer

Increase the Timeout property to 240 minutes. The Glue job failed due to a timeout after 2 hours, but the expected runtime is 3 hours. The default timeout for AWS Glue jobs is 2880 minutes (48 hours), but the CloudFormation template likely set a lower value. Increasing the Timeout property to 240 minutes (4 hours) provides enough time for the job to complete without being prematurely terminated.

Answer

Increase NumberOfWorkers to 20

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Set MaxRetries to 3

Answer

Change WorkerType to G.2X

Question 3

A data scientist is preparing a dataset for binary classification using SageMaker. The dataset has 100 features and 10,000 rows, but the target variable is highly imbalanced (95% negative, 5% positive). Which technique should the data scientist apply during data preparation to address the imbalance?

Accepted Answer

Apply SMOTE to generate synthetic samples for the minority class. SMOTE (Synthetic Minority Oversampling Technique) is the most appropriate technique because it generates synthetic samples for the minority class by interpolating between existing minority instances, which avoids the overfitting risk of simple duplication (oversampling) and the information loss from undersampling. In SageMaker, SMOTE can be applied during data preparation using libraries like imbalanced-learn before training, or via SageMaker Data Wrangler's built-in transform, making it a robust choice for handling class imbalance without discarding data.

Answer

Oversampling the minority class by duplicating examples

Answer

Collect more data to match the number of samples in both classes

Answer

Random undersampling of the majority class

Question 4

A machine learning engineer is using SageMaker Processing to run a scikit-learn preprocessing script. The script reads a CSV file from S3, applies a StandardScaler, and writes the output. The job fails with a 'MemoryError'. Which change should the engineer make to the data preparation process?

Accepted Answer

Increase the instance memory size for the processing job. The MemoryError indicates that the processing job's instance does not have enough RAM to hold the dataset and the intermediate results of the StandardScaler (which computes mean and variance in memory). Increasing the instance memory size (Option B) directly resolves this by providing more RAM for the scikit-learn operations. SageMaker Processing jobs allow you to choose instances with larger memory, such as the r5 or r6i families, to accommodate larger datasets.

Answer

Use a SageMaker Spark container instead of scikit-learn

Answer

Write the output as Parquet instead of CSV

Answer

Standardize the features before loading into the DataFrame

Question 5

A company uses AWS Glue to run ETL jobs that prepare data for machine learning. The source data in S3 has a schema that evolves over time (new columns are added occasionally). The Glue job schema is defined as a fixed schema in the job script. After an update to the source data, the Glue job fails with an error about mismatched schemas. How should the data engineer modify the data preparation process to handle schema evolution?

Accepted Answer

Modify the Glue job to use a dynamic frame and enable schema updates with a 'applyMapping' that includes new columns. Option A is correct because AWS Glue DynamicFrames natively handle schema evolution by allowing you to apply a mapping that can include new columns. By using `applyMapping` with `resolveChoice`, you can define how to handle new fields (e.g., cast to a type or keep as a struct), preventing job failures when the source schema changes. This avoids the rigidity of a fixed schema in the job script.

Answer

Run a Glue crawler before each job to update the Data Catalog, but keep the fixed schema in the job

Answer

Store the schema definition in a separate file in S3 and read it at runtime

Answer

Manually update the Glue job script each time the schema changes

Question 6

A data scientist is using SageMaker Data Wrangler to prepare features for a classification model. Which TWO statements about feature engineering in Data Wrangler are correct?

Accepted Answer

Transformations created in Data Wrangler can be exported as a SageMaker Processing script. Option C is correct because SageMaker Data Wrangler allows you to export the entire data flow, including all transformations, as a SageMaker Processing script. This script can be run at scale on managed infrastructure, enabling you to operationalize the feature engineering pipeline for training or inference without manual rework.

Answer

Data Wrangler only supports CSV and Parquet input formats

Answer

Data Wrangler enables writing custom PySpark transformations

Answer

Data Wrangler automatically scales features for XGBoost models

Question 7

A data engineer is optimizing Amazon Athena queries on large datasets stored in S3 for machine learning data preparation. Which THREE practices improve query performance?

Accepted Answer

Partition the data by a frequently filtered column, such as date. Partitioning by a frequently filtered column, such as date, allows Athena to use partition pruning. When a query includes a filter on the partition column, Athena can skip entire directories of data in S3, drastically reducing the amount of data scanned and improving query performance while also lowering cost.

Answer

Use uncompressed CSV files for simplicity

Answer

Partition the data by every column to maximize filtering

Question 8

A team is building a machine learning model for natural language processing using SageMaker BlazingText. The data preparation step must format the training data correctly. What format does BlazingText require for supervised text classification?

Accepted Answer

Tokenized words separated by spaces, with text and labels combined in a single line (e.g., '__label__positive great product'). BlazingText for supervised text classification expects the training data in a specific format where each line contains the text and its labels, with labels prefixed by '__label__'. This format allows BlazingText to efficiently parse and process the data for training the word2vec or classification model without additional preprocessing. Option C correctly describes this format, where the label and text are space-separated on a single line.

Answer

One-hot encoded feature vectors stored in CSV

Answer

JSON lines with a 'text' and 'label' field

Answer

TFRecord files with sequence features

Question 9

A company uses Amazon SageMaker Ground Truth to create labeled datasets for object detection. The output must be in COCO format for downstream model training. How should the data preparation process be configured?

Accepted Answer

Select 'Object Detection' task type and specify 'COCO' as the output format in the labeling job configuration. Option D is correct because Amazon SageMaker Ground Truth natively supports outputting object detection labeling jobs in COCO format. When you select 'Object Detection' as the task type, the labeling job configuration includes an option to specify 'COCO' as the output format, which automatically structures the labeled data into the required COCO JSON schema without any post-processing.

Answer

Use a built-in transformation to convert from Ground Truth JSON to COCO after labeling

Answer

Use a pre-built AWS Lambda function to transform annotations to COCO

Answer

Write a custom SageMaker Processing script to convert the output to COCO

Question 10

A machine learning engineer is using SageMaker Data Wrangler to perform data validation. Which step should be added to the pipeline to ensure data quality before training?

Accepted Answer

Apply a 'Data Quality' transformation in Data Wrangler to validate column statistics. Option B is correct because SageMaker Data Wrangler includes a built-in 'Data Quality' transformation that allows you to validate column statistics (e.g., missing values, min/max, distinct counts) directly within the visual pipeline. This step ensures data quality without requiring custom code or external services, integrating seamlessly with the Data Wrangler workflow for pre-training validation.

Answer

Write a custom SageMaker Processing job for validation

Answer

Use AWS Glue DataBrew to profile the dataset

Answer

Add a SageMaker Pipeline step to check data quality after Data Wrangler

Question 11

A data scientist is preparing a large dataset (50 GB) for training a TensorFlow model on SageMaker. The dataset consists of many small CSV files. Training is slow due to I/O bottlenecks. Which data preparation strategy most effectively accelerates training?

Accepted Answer

Convert the dataset to TFRecord format and use tf.data pipeline with prefetching. Option A is correct because TFRecord format stores data in a binary, row-oriented format that TensorFlow's tf.data API can read efficiently, especially with prefetching to overlap data loading with model computation. This eliminates the per-file open/parse overhead of many small CSV files, which is the primary cause of I/O bottlenecks in this scenario.

Answer

Convert the dataset to Parquet format and use Apache Arrow for loading

Answer

Compress the CSV files and decompress during data loading

Answer

Use a larger instance type with more vCPUs

Question 12

Refer to the exhibit. A data engineer runs an AWS Glue ETL job with the following script portion. The job fails with an error: 'An error occurred while calling o113.pyWriteDynamicFrame. No such file or directory'. What is the most likely cause?

Accepted Answer

The output S3 path is missing a trailing slash. The error 'No such file or directory' when calling `pyWriteDynamicFrame` typically occurs because AWS Glue expects the output S3 path to end with a trailing slash to denote a directory. Without it, Glue may interpret the path as a file name rather than a directory, leading to a failure when attempting to write the Parquet files. Adding a trailing slash (e.g., `s3://bucket/output/`) resolves the issue.

Answer

The output format 'parquet' is not supported by Glue

Answer

The input partition path is incorrect because it includes the partition key

Answer

The schema contains a column with a reserved name

Question 13

Refer to the exhibit. A SageMaker Processing job fails with the following error log. Which change during data preparation would resolve the issue?

Accepted Answer

Modify the preprocessing script to cast 'age' to float using astype(float). Option D is correct because the error log indicates a type mismatch when processing the 'age' column, likely due to mixed data types (e.g., strings and numbers) in a column expected to be numeric. By explicitly casting the column to float using astype(float) in the preprocessing script, you ensure consistent numeric type handling, which resolves the failure during SageMaker Processing job execution.

Answer

In SageMaker Data Wrangler, set the 'age' column type to 'number'

Answer

Drop rows with missing values in the 'age' column before training

Answer

Remove the 'age' column from the dataset entirely

Question 14

A data engineer is using SageMaker Pipelines to automate data preparation. Which TWO statements about data validation within a pipeline are correct?

Accepted Answer

The pipeline can be configured to fail if data quality checks do not meet thresholds. Option A is correct because SageMaker Pipelines allows you to define conditions that evaluate the output of data quality checks (e.g., using Amazon SageMaker Model Monitor or custom validation scripts). If the checks fail to meet specified thresholds (e.g., missing values exceed 5%), the pipeline can be configured to fail, stopping execution and preventing downstream steps from processing invalid data.

Answer

Data validation can only be performed on training data, not inference data

Answer

Data validation steps cannot pass results to subsequent steps

Answer

Data validation requires a trained model to evaluate predictions

Question 15

A company is building a time series forecasting model using SageMaker DeepAR. The raw data is a CSV with columns: timestamp, item_id, and value. What is the correct data format required for DeepAR training?

Accepted Answer

JSON Lines files with 'start', 'target', and optional fields per time series. DeepAR requires time series data to be provided in JSON Lines format, where each line represents a single time series with a 'start' timestamp (in ISO 8601 format), a 'target' array of values, and optional fields like 'cat' for categorical features. This structured format allows DeepAR to handle variable-length sequences and missing values natively, which is not possible with simple CSV or wide-format data.

Answer

A wide-format CSV where each column is a different time series

Answer

Parquet files with a schema containing timestamp, item_id, and value

Answer

A single CSV file with columns: timestamp, item_id, value