MLA-C01 Practice Test 39 — 15 Questions

Question 1

A company is preparing a large dataset for a SageMaker built-in XGBoost model. The dataset has missing values in both numeric and categorical features, and some categorical features have high cardinality. Which THREE data preparation steps should the company take to optimize model performance? (Choose three.)

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

Accepted Answer

Split the data into training, validation, and test sets before any imputation.. Splitting the data into training, validation, and test sets before any imputation prevents data leakage. If imputation statistics (e.g., mean, median) were computed on the full dataset, information from the validation and test sets would influence the training data, leading to overly optimistic performance estimates and poor generalization to new data.

Answer

Remove any rows with outlier values.

Answer

Apply target encoding to all categorical features regardless of cardinality.

Question 2

A company has a SageMaker real-time endpoint that serves predictions. They want to set up automated monitoring and remediation for when the number of 5XX errors exceeds a threshold. Which TWO steps should they take? (Choose TWO.)

Accepted Answer

Configure the CloudWatch Alarm to publish to an SNS topic. A CloudWatch Alarm on the 5XXError metric can be configured to publish to an SNS topic, enabling automated notifications or remediation actions (e.g., via Lambda) when the alarm state is triggered. This is the standard AWS approach for alerting on endpoint errors without custom polling.

Answer

Use SageMaker Model Monitor to detect 5XX errors

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Set up a scheduled EventBridge rule to check 5XXError every minute

Answer

Write a custom script on EC2 to poll the endpoint and check for errors

Question 3

A machine learning engineer is configuring auto-scaling for a SageMaker real-time endpoint. The endpoint is expected to have steady traffic during business hours and low traffic at night. The engineer wants to minimize costs by scaling in during low traffic, but the model container has a long start-up time (about 5 minutes). Which scaling policy should the engineer use to prevent request drops during sudden traffic spikes?

Accepted Answer

Use a target tracking scaling policy based on average invocations per minute with a warm-up of 300 seconds.. Target tracking scaling policies in SageMaker automatically adjust capacity to maintain a target metric value, and the warm-up time of 300 seconds accounts for the 5-minute container start-up latency. This prevents request drops during sudden traffic spikes by ensuring new instances are fully initialized before they receive traffic, while still allowing the endpoint to scale in during low traffic to minimize costs.

Answer

Use a step scaling policy based on invocations per minute with a step that adds two instances at a time.

Answer

Use a scheduled scaling action to add instances before business hours and remove them after.

Answer

Use a simple scaling policy based on average CPU utilization with a cooldown period of 5 minutes.

Question 4

A machine learning engineer needs to optimize a trained TensorFlow model for deployment on edge devices with limited compute. Which SageMaker feature should they use to compile the model for target hardware?

Accepted Answer

SageMaker Neo. SageMaker Neo is the correct choice because it is specifically designed to compile trained machine learning models into an optimized format for target hardware architectures, such as ARM, Intel, or NVIDIA, enabling efficient inference on edge devices with limited compute resources. It uses a compiler to apply hardware-specific optimizations like operator fusion and memory layout tuning, reducing latency and memory footprint without requiring manual code changes.

Answer

SageMaker Model Monitor

Answer

SageMaker Debugger

Answer

SageMaker Elastic Inference

Question 5

A machine learning engineer is using Amazon SageMaker Data Wrangler to create a data preparation pipeline. The pipeline includes multiple transforms such as handling missing values, scaling, and encoding. The engineer wants to export the prepared data directly to a feature group in Amazon SageMaker Feature Store for reuse in training and inference. Which export option should the engineer choose?

Accepted Answer

Export to a feature group in SageMaker Feature Store.. Amazon SageMaker Data Wrangler provides a built-in export destination for SageMaker Feature Store, allowing you to directly write the transformed data to a feature group without additional code. This enables seamless reuse of the prepared features for both training and real-time inference, leveraging the Feature Store's low-latency retrieval and versioning capabilities.

Answer

Export to Amazon S3 as a CSV file.

Answer

Export to a Jupyter notebook for further processing.

Answer

Export to Amazon Redshift for analysis.

Question 6

A company's ML pipeline runs in multiple AWS accounts (dev, test, prod). They want to enforce that only approved models from a central Model Registry can be deployed to the production account. Which combination of services is MOST appropriate to implement this governance?

Accepted Answer

AWS Organizations with SCPs, AWS CodePipeline with cross-account actions, and SageMaker Model Registry with approval status.. It combines AWS Organizations with SCPs to enforce cross-account deployment policies, AWS CodePipeline with cross-account actions to orchestrate the pipeline across dev/test/prod accounts, and SageMaker Model Registry with approval status to gate deployments to only approved models. This ensures that only models with an 'Approved' status in the central registry can be deployed to the production account, meeting the governance requirement.

Answer

AWS Config, Amazon GuardDuty, and AWS Security Hub.

Answer

Amazon API Gateway, AWS Step Functions, and Amazon DynamoDB.

Answer

AWS Service Catalog, AWS KMS, and AWS CloudTrail.

Answer

AWS CloudFormation StackSets, Amazon EventBridge, and AWS Lambda.

Question 7

A machine learning engineer is deploying a model using AWS Lambda for inference. The model is a small scikit-learn classifier with a size of 50 MB. The Lambda function is invoked by an API Gateway REST API. The engineer notices that cold starts are causing high latency. Which action would most effectively reduce cold start latency without increasing costs significantly?

Accepted Answer

Configure provisioned concurrency for the Lambda function.. Provisioned concurrency pre-initializes the Lambda execution environment, keeping it warm and ready to handle requests immediately. This eliminates the cold start overhead for the first request, directly reducing latency without incurring the ongoing costs of a larger memory allocation or the complexity of EFS/container management.

Answer

Store the model in Amazon EFS and load it at runtime.

Answer

Increase the Lambda function memory to the maximum of 10,240 MB.

Answer

Package the model in a container image and deploy using Lambda container support.

Question 8

A data engineer needs to provide the data science team with access to various data sources for machine learning. The team uses Amazon SageMaker Studio. Which TWO data sources can be accessed directly from SageMaker Studio notebooks without additional infrastructure? (Choose two.)

Accepted Answer

Amazon S3.. Amazon SageMaker Studio notebooks have a built-in SageMaker SDK that can directly read from and write to Amazon S3 using the `s3fs` filesystem or the SageMaker `s3_utils` module. This integration requires no additional infrastructure because S3 is the default storage backend for SageMaker, and the notebook environment is pre-configured with the necessary IAM roles and boto3 libraries to access S3 buckets directly.

Answer

Amazon Redshift.

Answer

Amazon DynamoDB.

Answer

Amazon RDS (MySQL).

Question 9

A startup wants to deploy a model that has variable traffic patterns, with some periods of no traffic and occasional spikes. They want to pay only for what they use and do not want to manage instances. Which SageMaker inference option should they choose?

Accepted Answer

Serverless inference. Serverless inference is the correct choice because it automatically scales to zero during periods of no traffic and scales up to handle spikes, charging only for the compute time used. This eliminates the need to manage underlying instances, making it ideal for variable and intermittent traffic patterns.

Answer

Batch transform

Answer

Real-time endpoint with auto-scaling

Answer

Multi-model endpoint

Question 10

A data scientist is performing feature selection for a high-dimensional dataset with 10,000 features. The goal is to identify a small subset of features that are most predictive of the target variable. The scientist wants to use a method that is computationally efficient and can handle feature interactions implicitly. Which feature selection method is most appropriate?

Accepted Answer

Lasso regularization (L1). Lasso regularization (L1) reduces coefficients of less important features to zero, effectively performing feature selection. It is computationally efficient for high dimensions and can handle interactions implicitly if polynomial features are added, but the base Lasso is linear. Among the given, Lasso is the best choice for efficiency and implicit selection. Mutual information is univariate, not handling interactions. Recursive feature elimination is expensive. Correlation analysis is also univariate.

Answer

Pearson correlation analysis

Answer

Mutual information

Answer

Recursive feature elimination (RFE)

Question 11

A machine learning team is building a model to predict customer churn. They have historical data that includes customer activity logs, each with a timestamp. The team wants to ensure that the training data does not contain any data leakage from the future. Which approach should they take when preparing the training and validation datasets?

Accepted Answer

Split the data by time, using data before a certain date for training and after for validation. Splitting by time (chronological split) prevents data leakage by ensuring that the validation set contains only future data relative to the training set. In time-series or timestamped data, random splits can allow the model to learn from future patterns, artificially inflating performance. This approach respects the temporal dependency inherent in customer churn prediction.

Answer

Use stratified sampling based on churn label

Answer

Randomly split the data 80/20 for training and validation

Answer

Use k-fold cross-validation with shuffling

Question 12

A data scientist is training a binary classification model using a dataset that has a severe class imbalance (90% negative, 10% positive). Which technique should be used to address the imbalance during model training?

Accepted Answer

Apply random oversampling of the minority class. Random oversampling of the minority class (Option C) directly addresses class imbalance by duplicating or synthesizing examples from the positive class, which balances the training distribution and prevents the model from becoming biased toward the majority class. This technique is specifically designed to mitigate the skewed gradient updates that occur when the minority class is underrepresented, leading to better recall and precision for the positive class in binary classification tasks.

Answer

Use a larger batch size

Answer

Use L2 regularization

Answer

Increase the learning rate

Question 13

A data scientist needs to create a feature group in Amazon SageMaker Feature Store for real-time recommendations. Which TWO configurations are required? (Select TWO.)

Accepted Answer

Specify a record identifier feature. Online store must be enabled for real-time serving, and a record identifier is required to uniquely identify records. Offline store and feature description are optional; time-to-live is not a standard feature.

Answer

Enable the offline store

Answer

Provide a feature description

Answer

Set a time-to-live (TTL) for the records

Question 14

A machine learning engineer needs to standardize features to have zero mean and unit variance before training a support vector machine. Which scaling method should they apply?

Accepted Answer

StandardScaler. StandardScaler transforms data to have zero mean and unit variance, which is required for SVM and many other algorithms.

Answer

Normalizer

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RobustScaler

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MinMaxScaler

Question 15

A data engineer wants to transform a categorical feature with 1,000 possible values into numerical features for a linear model. Which feature engineering technique is most appropriate for this high-cardinality feature?

Accepted Answer

Target encoding. Target encoding replaces each category with the mean of the target variable for that category, which handles high cardinality without exploding dimensionality. One-hot encoding creates 1,000 columns, which is problematic for linear models.

Answer

One-hot encoding

Answer

Ordinal encoding

Answer

Label encoding