MLA-C01 Multiple Select — 50 Questions

Question 1

A company wants to deploy a PyTorch model on SageMaker for real-time inference. Which two steps are required? (Select TWO.)

Accepted Answer

Package the model artifacts into a tar.gz file.. SageMaker requires model artifacts to be packaged as a single tar.gz file (containing the model weights, serialized PyTorch model, and any dependencies) for deployment. This compressed archive is uploaded to S3 and referenced when creating the model object for real-time inference.

Answer

Upload the training data to an S3 bucket.

Answer

Register the model in the SageMaker Model Registry.

Answer

Set up a SageMaker Notebook instance.

Question 2

A data engineer is using Amazon Athena to query a partitioned dataset stored in S3. Which THREE actions are necessary to ensure the queries can access the data and run efficiently?

Accepted Answer

Store the underlying data in a columnar format like Parquet. Storing data in a columnar format like Parquet reduces the amount of data scanned by Athena because it reads only the columns required by the query, not entire rows. This directly lowers query cost and improves performance, especially on large datasets, as Parquet also supports compression and predicate pushdown.

Answer

Create an AWS Glue DataBrew recipe to transform the data

Answer

Add each partition manually using ALTER TABLE ADD PARTITION

Question 3

Which TWO actions are recommended best practices for securing an Amazon SageMaker notebook instance? (Select TWO.)

Accepted Answer

Use AWS KMS to encrypt the notebook instance's storage volume.. Encrypting the notebook instance's storage volume with AWS KMS ensures data-at-rest protection, which is a fundamental security best practice. SageMaker notebook instances use Amazon EBS volumes for storage, and KMS encryption safeguards sensitive code, datasets, and model artifacts stored on that volume against unauthorized access.

Answer

Use network ACLs to restrict API calls to the SageMaker API.

Answer

Enable Multi-AZ deployment for the notebook instance.

Answer

Associate the notebook instance with a public subnet that has an internet gateway.

Question 4

A data scientist is training a deep learning model using SageMaker and wants to use distributed training across multiple GPUs to reduce training time. Which TWO actions should the scientist take to configure distributed training? (Select TWO.)

Accepted Answer

Use the SageMaker distributed data parallelism library. The SageMaker distributed data parallelism library (option B) automatically partitions training data and synchronizes gradients across multiple GPUs, reducing training time without manual data splitting. Configuring the SageMaker estimator with a distribution parameter (option D) enables this library by specifying the distribution strategy (e.g., 'torch_distributed' or 'tensorflow_distributed'), which is required to activate distributed training.

Answer

Reduce the number of epochs to match the number of GPUs

Answer

Manually split the training data into shards and upload to S3

Answer

Set the instance count to 1 with a multi-GPU instance

Question 5

A data engineer is using AWS Glue to prepare a dataset for ML. The engineer wants to split the dataset into training and testing sets while preserving the distribution of the target variable. Which TWO methods achieve this goal? (Select TWO)

Accepted Answer

Use the `train_test_split` function from scikit-learn in a SageMaker notebook. The `train_test_split` function from scikit-learn supports the `stratify` parameter, which preserves the distribution of the target variable when splitting a dataset into training and testing sets. This is a standard, reliable method for stratified splitting in Python-based ML workflows, and it can be used directly in a SageMaker notebook.

Answer

Use Amazon Athena to create views with random sampling

Answer

Use AWS Glue's built-in random split transform

Answer

Use Amazon SageMaker's built-in SplitType parameter in a Processing Job

Question 6

A data team is preparing data for a machine learning pipeline. Which TWO practices are best for ensuring data quality and reproducibility? (Choose two.)

Accepted Answer

Implement automated data validation checks to catch anomalies in new data.. Automated data validation checks (e.g., using AWS Glue DataBrew or Deequ on Amazon EMR) proactively catch schema drift, missing values, and distribution anomalies in new data, ensuring that only high-quality data enters the ML pipeline. This practice is essential for maintaining data quality at scale without manual intervention.

Answer

Use a fixed random seed when sampling data to ensure repeatability.

Answer

Shuffle the dataset before splitting into train and test sets.

Answer

Manually inspect and clean data to remove outliers.

Question 7

A company wants to deploy a trained model to a SageMaker endpoint with automatic scaling based on traffic. Which TWO configurations are required? (Choose two.)

Accepted Answer

Set up an Application Auto Scaling policy. Application Auto Scaling is the AWS service that automatically adjusts the number of instances for a SageMaker endpoint based on demand. You define a scaling policy (e.g., target tracking, step scaling) that tells Auto Scaling when to add or remove instances, which is essential for handling variable traffic without manual intervention.

Answer

Use a multi-model endpoint

Answer

Enable data capture

Answer

Configure a lifecycle configuration

Question 8

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. 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 9

A company wants to deploy its trained model to edge devices such as cameras and IoT devices. The model must run efficiently with low latency and minimal memory footprint. Which THREE actions should the company take to prepare the model for edge deployment? (Choose THREE.)

Accepted Answer

Use SageMaker Edge Manager to package and manage the model on devices.. SageMaker Edge Manager is purpose-built to package, optimize, and manage machine learning models on edge devices. It provides model packaging, runtime monitoring, and over-the-air updates, ensuring the model runs efficiently with low latency and minimal memory footprint on resource-constrained hardware like cameras and IoT devices.

Answer

Increase the model's complexity to improve accuracy on edge devices.

Answer

Deploy the model directly as a SageMaker endpoint and have the edge devices call it over the internet.

Question 10

A data engineer is building a feature engineering pipeline in AWS Glue ETL to process streaming data from Amazon Kinesis. The data includes a nested JSON structure with arrays. The engineer needs to flatten the nested structures into a tabular format for machine learning. Which THREE approaches are valid for this task? (Choose 3.)

Accepted Answer

Use Python's json.loads in a map function. Python's json.loads can be used within a PySpark map function to parse nested JSON strings from streaming data in AWS Glue ETL. This allows you to extract and flatten nested fields into a tabular structure by iterating over each record and converting the JSON into a flat dictionary, which can then be mapped to DataFrame columns.

Answer

Use Athena's UNNEST function on the raw data

Answer

Use Amazon SageMaker Processing with scikit-learn

Question 11

A machine learning engineer is building a pipeline to ingest data from multiple sources into an Amazon S3 data lake. The data includes both batch files and real-time streams. The engineer needs to catalog the data for discovery and querying. Which TWO AWS services should the engineer use to ingest and catalog the data? (Select TWO.)

Accepted Answer

Amazon S3. S3 is the data lake storage; AWS Glue Data Catalog stores metadata for discovery and querying. AWS Glue ETL can ingest batch data; Kinesis Firehose ingests streaming data. Athena is for querying, not ingestion or cataloging; Redshift is a data warehouse.

Answer

Amazon Redshift

Answer

Amazon Athena

Answer

AWS Glue ETL

Question 12

A data scientist is using Amazon SageMaker Data Wrangler to create a data flow for a machine learning project. The source data is in Amazon S3 and contains PII (personally identifiable information) such as email addresses and credit card numbers. The data scientist needs to prepare the data for training while ensuring compliance with data privacy regulations. Which THREE actions should the data scientist take? (Select THREE.)

Accepted Answer

Use Data Wrangler to redact or remove PII columns from the dataset before training.. Amazon SageMaker Data Wrangler provides built-in transforms to redact or remove PII columns, which directly addresses compliance requirements by eliminating sensitive data from the training dataset. This is a straightforward and effective method to prevent PII from being used in model training, reducing the risk of data exposure.

Answer

Include the raw PII in the training dataset and rely on the model to not memorize it.

Answer

Use AWS Glue to copy the data to a separate bucket without any transformations.

Question 13

A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality. Which THREE actions are necessary? (Choose three.)

Accepted Answer

Enable data capture on the endpoint. Enabling data capture on the SageMaker endpoint is a prerequisite for monitoring data drift and model quality. Data capture automatically records input requests and output responses from the endpoint, which SageMaker Model Monitor later analyzes against a baseline to detect drift. Without data capture, there is no data to compare against the baseline constraints.

Answer

Deploy a shadow endpoint for comparison

Answer

Use SageMaker Debugger for monitoring

Question 14

A team is migrating their ML infrastructure to AWS and wants to use infrastructure as code to manage SageMaker Studio domains, user profiles, and associated resources. Which services can they use for this purpose? (Select THREE.)

Accepted Answer

AWS CDK (Cloud Development Kit). AWS CDK (Cloud Development Kit) is correct because it allows you to define AWS infrastructure, including SageMaker Studio domains and user profiles, using familiar programming languages like Python or TypeScript. CDK synthesizes these definitions into CloudFormation templates, enabling infrastructure as code (IaC) for SageMaker resources. This approach provides type safety and high-level abstractions, making it suitable for managing complex ML environments.

Answer

SageMaker Python SDK

Answer

Boto3

Question 15

A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices should they follow to optimize training performance? (Choose three.)

Accepted Answer

Store training data in Amazon S3 in a shuffled and compressed format. Storing training data in Amazon S3 in a shuffled and compressed format (Option A) optimizes training performance because shuffling prevents biased gradient updates during stochastic gradient descent, while compression reduces I/O overhead and network transfer time. SageMaker's Pipe mode can then stream this compressed data directly to the training algorithm without intermediate disk writes, further accelerating throughput.

Answer

Use the largest instance type available

Answer

Increase the number of layers in the model to improve accuracy

Question 16

A data scientist is evaluating data quality for a machine learning project. The dataset has missing values, outliers, and inconsistent formatting. Which TWO steps should the data scientist perform during the data preparation phase? (Choose 2.)

Accepted Answer

Standardize numeric features. Standardizing numeric features (Option C) is a critical data preparation step because it rescales features to have zero mean and unit variance, which prevents features with larger magnitudes from dominating distance-based algorithms like k-nearest neighbors or gradient descent optimization. This transformation is essential for many machine learning models to converge faster and perform correctly.

Answer

Normalize text data to lowercase

Answer

Remove all outliers blindly

Answer

Use a large neural network to handle all transformations

Question 17

A team is deploying a model using SageMaker Pipelines. They have defined a pipeline with steps: preprocessing, training, evaluation, and conditional registration. The evaluation step produces a JSON file with metrics. If accuracy > 0.9, the model is registered; else, the pipeline fails. Which TWO statements about this pipeline are correct? (Choose TWO.)

Accepted Answer

The evaluation step must output a JSON file in a specific format to be used by the condition step.. The SageMaker Pipelines condition step expects the evaluation step to output a JSON file with a specific format, typically containing a metrics dictionary. The condition step then uses a property file to extract the accuracy value from that JSON, enabling the conditional logic to evaluate whether accuracy > 0.9.

Answer

The conditional step should be implemented as a separate Lambda function called from the pipeline.

Answer

The pipeline will automatically retry the training step if the condition fails.

Answer

The model registration step should be placed before the condition step to ensure the model is always registered.

Question 18

A data scientist wants to monitor a deployed model for performance degradation. Which TWO metrics from Amazon CloudWatch should they use to detect issues? (Select two.)

Accepted Answer

ModelLatency. (ModelLatency) is correct because it measures the time taken for the model to respond to inference requests, and a sudden increase in latency can indicate performance degradation due to resource contention, model drift, or infrastructure issues. Option D (Invocation5XXErrors) is correct because a rise in 5XX HTTP errors from the SageMaker endpoint signals that the model is failing to process requests, often due to out-of-memory errors, timeouts, or internal faults, directly reflecting degraded service health.

Answer

ModelQuality

Answer

CpuUtilization

Answer

InvocationCount

Question 19

A data scientist is using Amazon SageMaker Feature Store to manage features for a fraud detection model. The model needs to be updated hourly with new features from streaming data, and historical features must be available for training. Which THREE actions are necessary to set up this pipeline? (Choose THREE.)

Accepted Answer

Use point-in-time queries from the offline store to retrieve historical feature values for training. To ingest streaming features, use Kinesis Data Streams/Firehose. To store them, create a feature group with both online and offline stores. To retrieve historical data, use point-in-time queries from the offline store. The online store alone cannot serve historical queries. Ground Truth is for labeling, not feature ingestion. Data Wrangler is for preparation, not ingestion.

Answer

Use Amazon SageMaker Data Wrangler to continuously ingest streaming data into the feature group

Answer

Use Amazon SageMaker Ground Truth to label incoming streaming data

Question 20

A machine learning team is handling a text classification task with a dataset of 1 million documents. They need to convert text into numerical features. Which THREE techniques are commonly used for feature extraction from text? (Select THREE.)

Accepted Answer

Word embeddings (e.g., Word2Vec). TF-IDF, word embeddings (e.g., Word2Vec, GloVe), and bag-of-words are standard text vectorization methods. One-hot encoding is for categorical features, not text. PCA is for dimensionality reduction after vectorization.

Answer

One-hot encoding

Answer

Principal component analysis (PCA)