MLA-C01 Practice Test 44 — 15 Questions

Question 1

A machine learning team at a bank is training a binary classification model using SageMaker's built-in XGBoost algorithm on a dataset with 20 million rows and 300 features. They need to reduce training time while maintaining model accuracy. The data is stored in Amazon S3 as CSV files. Which approach should they take to speed up training?

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

Accepted Answer

Use SageMaker Pipe mode with the RecordIO protobuf format instead of File mode with CSV.. SageMaker's built-in XGBoost can consume data in Pipe mode with RecordIO protobuf, which streams data directly from S3 and reduces I/O overhead, leading to faster training on large datasets. File mode with CSV requires downloading all data to disk, which is slower. Distributed training options for XGBoost are limited and not configured via parameter_distribution. Thus, switching to Pipe mode with RecordIO is the recommended approach.

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Use SageMaker Processing to preprocess the data into a single large CSV file and then train with File mode.

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Increase the number of instances in the training cluster and enable distributed training with the parameter_distribution parameter set to 'fully'.

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Convert the CSV files to TFRecord format and use File mode to load the data.

Question 2

A data scientist is preparing a CSV dataset in Amazon S3 for a SageMaker training job. Several rows contain missing values in numeric feature columns, and the chosen algorithm cannot handle NaNs. The scientist wants a repeatable, code-based transformation that runs inside a SageMaker Processing job before training. Which step is the MOST appropriate?

Accepted Answer

Write a preprocessing script that uses pandas to impute or drop missing values and run it in a SageMaker Processing job with a scikit-learn container.. Running a pandas-based cleaning script in a SageMaker Processing job makes the imputation or row removal reproducible and code-driven, and the job reads from and writes to S3 so the cleaned dataset is available to training. This is the canonical pattern for repeatable preprocessing that must run before a training job and be re-executed as data changes.

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Use an S3 Lifecycle rule to expire objects containing missing values so only clean data remains in the bucket.

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Open the CSV in a SageMaker notebook, manually edit the missing cells, and save the file back to S3.

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Configure the SageMaker training job's input channel with a content type that tells the algorithm to ignore NaN values automatically.

Question 3

A machine learning engineer has trained a model in SageMaker and wants to deploy it to a real-time endpoint for low-latency inference. The model artifacts are stored in Amazon S3, and the engineer needs to create the endpoint with the least operational effort. Which sequence of actions should the engineer take?

Accepted Answer

Create a model, create an endpoint configuration, and create an endpoint using the SageMaker API or the AWS SDK for Python (Boto3).. A SageMaker real-time endpoint is composed of a model, an endpoint configuration, and an endpoint. Creating these three resources through the SageMaker API or Boto3 is the direct, low-effort way to deploy artifacts from Amazon S3 for low-latency inference. Batch transform, model registry pipelines, and self-managed EC2 hosting either do not provide real-time serving or add unnecessary operational burden.

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Package the model into a Docker image, push it to Amazon ECR, and run it on an Amazon EC2 instance behind an Application Load Balancer.

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Create a batch transform job that reads from Amazon S3 and writes predictions to Amazon S3, then expose the output as a REST API.

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Register the model in the SageMaker model registry and deploy it through a SageMaker pipeline with a manual approval step.

Question 4

A machine learning engineer is preparing a dataset for training a model on Amazon SageMaker. The dataset contains numerical features with varying scales, and the engineer wants to ensure that all features contribute equally during training. Which data preparation step should the engineer take?

Accepted Answer

Apply feature scaling, such as standardization or normalization, to the numerical features.. Feature scaling adjusts the range or distribution of numerical features so that they are on a similar scale. This is crucial for algorithms that rely on distance calculations or gradient descent, as unscaled features can lead to slow convergence or biased results. Standardization or normalization are appropriate methods to achieve equal contribution from all features.

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Apply one-hot encoding to all numerical features.

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Remove outliers from the dataset.

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Apply principal component analysis (PCA) to reduce dimensionality.

Question 5

A data engineer is preparing a dataset in Amazon SageMaker Data Wrangler for a binary classification model. The dataset contains missing values in several numeric columns, and the engineer wants a reusable, reproducible transformation that can be applied identically to the training data and to future inference data. The engineer plans to export the transformation and integrate it into a SageMaker Pipeline. Which TWO actions should the engineer take to ensure the imputation is consistent between training and inference? (Choose two.)

Accepted Answer

Configure the imputation transform in the Data Wrangler flow so it learns the fill statistic (such as the mean) from the training data during the flow run.. Consistency between training and inference requires that the imputation statistic be learned from training data and that the same transformation be reused at inference. Configuring Data Wrangler to learn the fill statistic from training data captures the parameters, and exporting the flow into a SageMaker Pipeline processing step ensures those exact parameters and logic are applied to both training and inference inputs.

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Replace missing numeric values with zero in both the training and inference datasets using a custom Python script.

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Compute the mean of each numeric column on the full dataset including inference data, then hard-code those values into the pipeline.

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Delete all rows that contain missing values from the training dataset before training.

Question 6

A data scientist is using SageMaker Data Wrangler to prepare a dataset for a binary classification model. The dataset contains a mix of numerical and categorical features. The scientist wants to perform feature engineering to improve model performance. Which TWO actions are appropriate for handling categorical features in Data Wrangler? (Choose two.)

Accepted Answer

Apply one-hot encoding to categorical features with low cardinality.. Categorical features need to be encoded into numerical representations before training. One-hot encoding is effective for low-cardinality features, creating binary indicators. Target encoding is suitable for high-cardinality features, replacing categories with target statistics and reducing dimensionality. Both are available as transforms in SageMaker Data Wrangler. Scaling, PCA, and log transformations are designed for numerical features and are not applicable to categorical data.

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Apply PCA to categorical features for dimensionality reduction.

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Apply log transformation to categorical features.

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Apply min-max scaling to categorical features.

Question 7

A data engineer is using Amazon SageMaker Data Wrangler to prepare a dataset for a machine learning model. The engineer needs to ensure that the data preparation flow is reproducible and can be integrated into an MLOps pipeline. Which TWO actions should the engineer take to achieve this? (Choose two.)

Accepted Answer

Export the Data Wrangler flow to a SageMaker Pipeline as a processing step.. To ensure reproducibility and MLOps integration, the Data Wrangler flow should be exported either as a SageMaker Pipeline processing step or as a Python script committed to version control. Both methods capture the data preparation logic in a versionable, executable form that can be automated and tracked, meeting the requirements.

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Manually document each transformation step in a text file and apply them using a Jupyter notebook.

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Use the Data Wrangler interface to directly train a model without exporting the flow.

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Use Data Wrangler's built-in scheduling feature to run the flow daily.

Question 8

A machine learning engineer needs to split a dataset into training, validation, and test sets for a SageMaker training job. The dataset is stored in Amazon S3 as a single CSV file. The engineer wants to ensure that the splits are reproducible and that the test set is never used during training or hyperparameter tuning. Which approach should the engineer use?

Accepted Answer

Manually split the CSV file using a Python script with a fixed random seed, write the three splits to separate S3 prefixes, and use the training and validation prefixes in the training job, keeping the test prefix for final evaluation.. To ensure reproducibility and prevent data leakage, the engineer should split the data with a fixed random seed and store each split in separate S3 locations. The training job should only consume the training and validation sets, while the test set is reserved for final model evaluation. This is a standard best practice for ML workflows.

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Use the SageMaker training job's built-in data splitting feature by specifying a validation split percentage in the hyperparameters.

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Use SageMaker Data Wrangler to split the data into three parts and export them to S3, then use all three parts in the training job with different channels.

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Use Amazon Athena to run a query that randomly assigns rows to three splits, and store the results in S3. Then use all three splits in the training job.

Question 9

A machine learning engineer has trained a scikit-learn model and saved it as model.joblib in Amazon S3. The engineer wants SageMaker to host the model for real-time inference without writing a custom container or inference script, because the model uses only standard predict behavior. Which deployment approach should the engineer use?

Accepted Answer

Deploy the model with the SageMaker Scikit-learn built-in framework container using the SageMaker Python SDK SKLearnModel class, pointing model_data to the S3 artifact.. Because the model is a standard scikit-learn artifact and no custom inference logic is required, the built-in Scikit-learn framework container is the correct fit. Passing the S3 model artifact to the SKLearnModel class lets SageMaker load the joblib file and use the container's default predict handler, producing a real-time endpoint without custom code or image maintenance.

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Create a custom Docker image that installs scikit-learn, push it to Amazon ECR, and write an inference handler for the /invocations route.

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Use SageMaker Batch Transform with the built-in Scikit-learn container and invoke the endpoint for each real-time request.

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Upload model.joblib to SageMaker Model Registry and rely on automatic endpoint creation when the model package is approved.

Question 10

A machine learning team is deploying a model to a SageMaker endpoint and needs to implement A/B testing between two model versions. They want to split traffic 80/20 and monitor performance metrics for each variant. Which two actions should they take? (Choose two.)

Accepted Answer

Create a SageMaker endpoint configuration with two production variants, each specifying a different model and initial weight.. To perform A/B testing on SageMaker, you create an endpoint configuration with multiple production variants, each with a different model and weight to split traffic. Enabling data capture logs the requests and responses for each variant, allowing you to analyze performance metrics. Other options either do not provide native A/B testing or add unnecessary complexity.

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Configure an Application Load Balancer to distribute traffic between two separate endpoints.

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Use SageMaker Model Monitor to automatically compare the accuracy of the two variants.

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Create two separate SageMaker endpoints and use AWS Lambda to route requests based on a random number.

Question 11

An ML engineer has a real-time SageMaker endpoint serving a fraud-detection model. The team wants to release a new model version to a small percentage of live traffic first, monitor CloudWatch metrics for accuracy regressions, and roll back quickly if performance degrades. They also want the production and candidate variants to share the same endpoint so latency comparisons are apples-to-apples. Which SageMaker deployment strategy should they use?

Accepted Answer

Configure the endpoint with two production variants (current and candidate) and set initial variant weights, using CloudWatch alarms to trigger rollback.. A single SageMaker endpoint can host multiple production variants, each with its own model artifact and a traffic weight that you adjust without redeploying infrastructure. This provides canary-style exposure, apples-to-apples latency on shared instances, and a fast rollback path when CloudWatch alarms fire. It is the native SageMaker mechanism for controlled model releases with live monitoring, matching every stated requirement.

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Update the existing endpoint in place by replacing the production variant's model with the new model artifact.

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Create a second endpoint with the new model and use Route 53 weighted routing to split traffic.

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Deploy the new model as a production variant on the existing endpoint and configure a shadow variant with zero traffic weight.

Question 12

A data engineer is preparing a dataset for a time series forecasting model. The dataset contains a timestamp column and a target variable. The engineer wants to create additional features such as lag values and rolling averages. Which SageMaker Data Wrangler transform should be used to generate these time series features?

Accepted Answer

Use the 'Time Series' transform to create lag and rolling window features.. Time series feature engineering often involves creating lag features (past values) and rolling statistics (e.g., moving averages) to capture temporal patterns. In SageMaker Data Wrangler, the 'Time Series' transform provides a dedicated set of operations for these tasks, including lag, rolling window, and date part extraction. This transform simplifies the process and ensures correct handling of time order. Other transforms like text featurization or outlier handling serve different purposes and cannot produce these features.

Answer

Use the 'Handle Outliers' transform to create lag features.

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Use the 'Balance Data' transform to generate rolling averages.

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Use the 'Featurize Text' transform to extract date parts.

Question 13

A machine learning team runs a SageMaker AI Pipeline that trains a model and registers it in the SageMaker AI Model Registry. A separate deployment process must promote the model to production only after a human reviewer approves the model version. The team wants to automate the promotion so that approval in the Model Registry triggers the deployment without manual intervention. Which combination of steps should the engineer implement?

Accepted Answer

Configure an Amazon EventBridge rule for the SageMaker AI Model Registry model version state change to Approved, and use it to start a deployment workflow.. Model approval in the SageMaker AI Model Registry changes the model version state and emits an event. Capturing that event with Amazon EventBridge and routing it to a deployment workflow links the human approval gate to automated promotion, which is exactly the required behavior.

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Use a SageMaker AI Projects template that automatically deploys every model version as soon as it is registered.

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Schedule an AWS Lambda function every minute to call DescribeModelPackage and deploy any approved model version.

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Add a ConditionStep to the existing pipeline that checks the model version status and deploys the model.

Question 14

A machine learning team stores training data in an Amazon S3 bucket and wants to catalog it so that Amazon Athena and Amazon SageMaker Feature Store can discover the schema. The data is partitioned by year, month, and day in Hive-style prefixes, and new partitions are added daily. A data engineer must ensure new partitions are automatically discoverable without manual intervention. Which solution meets these requirements?

Accepted Answer

Create an AWS Glue crawler with a daily schedule that points at the S3 prefix and updates the Data Catalog.. A scheduled AWS Glue crawler is the standard mechanism for automatically discovering new Hive-style partitions in S3 and refreshing the Data Catalog. Because it runs on a daily schedule, new year/month/day partitions appear in the catalog without any manual command, and Athena and other catalog-aware services can query them immediately.

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Enable S3 Inventory and point Athena at the inventory report to enumerate partitions.

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Run MSCK REPAIR TABLE on an Athena table each time new data arrives.

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Register the S3 prefix as a SageMaker Feature Store offline store and let it infer the partitions.

Question 15

A machine learning engineer is deploying a model to a SageMaker endpoint that must handle occasional large payloads up to 1 GB. The inference time can take up to 10 minutes. The team wants to minimize cost and avoid idle compute. Which deployment option is most appropriate?

Accepted Answer

SageMaker Asynchronous Inference. Asynchronous Inference is designed for large payloads (up to 1 GB) and long inference times (up to 15 minutes). It queues requests and returns results via S3, and it can scale to zero when idle, reducing cost. Real-time and serverless inference have payload and timeout limits, and batch transform is for offline batch processing, not on-demand requests.

Answer

SageMaker real-time endpoint with automatic scaling

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SageMaker Serverless Inference

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SageMaker batch transform