MLA-C01 Practice Test 2 — 25 Questions

Question 1

A company deploys a machine learning model as a SageMaker real-time endpoint. They need to implement a mechanism to automatically roll back to the previous model version if performance degrades after a deployment. Which approach should they use?

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

Accepted Answer

Configure the SageMaker endpoint deployment with traffic shifting and set up CloudWatch alarms to trigger automatic rollback. SageMaker endpoints support deployment with traffic shifting (e.g., canary or linear patterns) via the 'DeploymentConfig' parameter, and you can attach CloudWatch alarms to the endpoint's variant metrics. If the alarm triggers (e.g., due to increased error rate or latency), SageMaker automatically rolls back the traffic to the previous model version, ensuring minimal manual intervention and fast recovery.

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Manually update the endpoint to point to the previous model version

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Create multiple endpoints and use Amazon Route 53 weighted routing to shift traffic

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Use AWS CodeDeploy with Amazon EC2 instances behind an Elastic Load Balancer

Question 2

A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to access an S3 bucket that contains sensitive data. The team wants to follow the principle of least privilege. How should the team grant access?

Accepted Answer

Create an IAM role with S3 access and assign it as the SageMaker execution role for the training job.. SageMaker training jobs use an IAM execution role to grant permissions to AWS services like S3. By creating a dedicated IAM role with only the necessary S3 actions (e.g., s3:GetObject, s3:PutObject) and assigning it as the SageMaker execution role, the team follows the principle of least privilege. SageMaker automatically assumes this role via AWS Security Token Service (STS) to access the S3 bucket on behalf of the container, without embedding credentials.

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Attach an IAM instance profile to the training instance with permissions to the bucket.

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Configure an S3 bucket policy that grants access to the training job's ARN.

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Store AWS access keys in the container image and use them to access the bucket.

Question 3

Refer to the exhibit. A user has the above IAM policy attached but cannot access files in SageMaker Studio. What additional permission is most likely needed?

Accepted Answer

s3:GetObject on the relevant S3 buckets. The IAM policy shown likely grants permissions for SageMaker API actions (e.g., CreateApp, DescribeDomain) but does not include S3 data plane permissions. SageMaker Studio notebooks and experiments read training data, scripts, and model artifacts from S3 buckets. Without s3:GetObject on the relevant S3 buckets, the user can launch the Studio environment but cannot load or save any files, resulting in access failures.

Answer

sagemaker:ListApps

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sagemaker:DescribeUserProfile

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kms:Decrypt

Question 4

A data scientist trained a model using SageMaker and wants to automate the retraining process when new data becomes available. Which AWS service is best suited to trigger a SageMaker training job based on an S3 event?

Accepted Answer

Amazon EventBridge with a rule matching S3 object creation.. Amazon EventBridge is the correct choice because it can directly capture S3 events (such as ObjectCreated) via a rule and invoke a SageMaker training job as a target. This event-driven architecture eliminates the need for polling or scheduled checks, enabling immediate retraining when new data arrives in S3.

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AWS Step Functions with a scheduled trigger.

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Amazon Simple Workflow Service (SWF) decider.

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Amazon Simple Queue Service (SQS) with a polling script.

Question 5

A company has a batch transform job in Amazon SageMaker that processes large datasets every night. Recently, the job has been failing sporadically with an out-of-memory error. The data size has not increased. What is the MOST likely cause?

Accepted Answer

The custom inference code has a memory leak that gradually consumes available memory.. A memory leak in custom inference code would cause gradual memory consumption over time, eventually leading to out-of-memory errors even if the dataset size remains unchanged. SageMaker batch transform jobs run inference on each record independently, so a leak that accumulates across records (e.g., not releasing tensors or file handles) would cause sporadic failures as the job processes more data.

Answer

The data distribution has shifted, causing different memory usage patterns.

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The instance type is not large enough to handle the dataset.

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The batch transform input data has increased in size.

Question 6

A machine learning team trains a model in SageMaker and wants to track every step — from dataset version to hyperparameters to final model artifact — for reproducibility and audit compliance. Which SageMaker feature should they use?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking is the correct choice because it is specifically designed to create a directed acyclic graph (DAG) of every step in the ML workflow, including dataset versions, hyperparameters, training jobs, and model artifacts. This enables full reproducibility and audit compliance by capturing the provenance of each entity and their relationships, which is exactly what the question requires.

Answer

SageMaker Feature Store

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SageMaker Experiments

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SageMaker Model Registry

Question 7

A financial services company needs to deploy a machine learning model for real-time fraud detection. The model must be highly available across multiple Availability Zones and must support automatic scaling based on request volume. The company also needs to perform canary deployments to test new model versions with a small percentage of traffic before full rollout. Which SageMaker feature should they use?

Accepted Answer

SageMaker real-time endpoint with production variants. SageMaker real-time endpoints with production variants enable canary deployments by routing a small percentage of traffic to a new model version while the majority goes to the current version. This feature also supports multi-AZ deployment for high availability and automatic scaling based on request volume via Application Auto Scaling, meeting all the stated requirements.

Answer

SageMaker Multi-Model Endpoint

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SageMaker Batch Transform

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SageMaker Serverless Inference

Question 8

A data science team has trained a PyTorch model for real-time inference and needs to deploy it on AWS with GPU acceleration while minimizing cold-start latency. Which SageMaker inference option should they choose?

Accepted Answer

Real-time endpoint with ml.g4dn instance. Real-time endpoints with GPU instances (e.g., ml.g4dn) provide low latency and support GPU acceleration, suitable for interactive inference. Serverless inference does not support GPU instances, asynchronous inference is for non-real-time, and batch transform is for offline predictions.

Answer

Serverless inference

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Batch transform

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Asynchronous inference endpoint

Question 9

A financial services firm is training a fraud detection model using SageMaker. The dataset is highly imbalanced (0.1% fraudulent transactions). The model currently achieves 99.9% accuracy but only catches 5% of fraud cases. Which metric should the team prioritize to evaluate model performance?

Accepted Answer

Recall. Recall is the proportion of actual fraud cases that the model correctly identifies, so it directly measures the model's ability to catch fraud. With only 5% of fraud cases caught, recall is extremely low, and improving it is the priority. Accuracy is misleading here because a model that predicts 'not fraud' for every transaction would still achieve 99.9% accuracy on a 0.1% fraud dataset.

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Accuracy

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Precision

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F1-score

Question 10

A machine learning team needs to deploy a model that was built using scikit-learn. They want to use SageMaker for hosting. Which approach should they take?

Accepted Answer

Package the model artifacts and use the SageMaker built-in scikit-learn container for inference. SageMaker provides a pre-built, optimized Docker container for scikit-learn that supports inference. By packaging the model artifacts (e.g., a joblib or pickle file) and deploying them using the built-in container, the team avoids the overhead of custom container creation while ensuring compatibility with SageMaker's hosting infrastructure, including automatic scaling and load balancing.

Answer

Create a Jupyter notebook that loads the model and runs predictions on the SageMaker notebook instance

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Create a custom Docker container with scikit-learn and deploy it on SageMaker

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Launch a SageMaker training job with the model and use the training instance as an endpoint

Question 11

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.

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Undersample the majority class to create a balanced dataset, then split.

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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 12

A healthcare company is building a model to predict patient readmission rates. The dataset contains a mix of numeric features (age, blood pressure, lab test results) and categorical features (gender, diagnosis code, hospital department). The dataset has 2 million rows. The data is stored in an Amazon S3 bucket, and they use AWS Glue to catalog and preprocess the data. The data scientist notices that the 'diagnosis_code' column has 10,000 unique codes, and 20% of the rows have missing values for 'blood_pressure'. They plan to use a SageMaker built-in XGBoost model. For optimal model performance, which preprocessing steps should they apply using AWS Glue ETL?

Accepted Answer

Impute missing 'blood_pressure' with median, and apply integer encoding to 'diagnosis_code'.. XGBoost handles missing values natively, so median imputation for 'blood_pressure' is robust to outliers and preserves data distribution, while integer encoding (label encoding) for 'diagnosis_code' with 10,000 unique values is efficient and avoids the dimensionality explosion of one-hot encoding. AWS Glue ETL can apply these transformations using built-in functions like `Imputer` and `StringIndexer` without excessive memory overhead.

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Impute missing 'blood_pressure' with the mean, and apply label encoding to 'diagnosis_code'.

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Replace missing 'blood_pressure' with -1 and apply one-hot encoding to 'diagnosis_code' after grouping rare codes into 'other'.

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Apply one-hot encoding to 'diagnosis_code' and drop rows with missing 'blood_pressure'.

Question 13

A company has deployed a machine learning model on Amazon SageMaker and wants to automatically detect when the distribution of input features deviates significantly from the training data distribution. Which SageMaker feature should they use?

Accepted Answer

SageMaker Model Monitor – Data Quality Monitoring. SageMaker Model Monitor – Data Quality Monitoring is the correct choice because it is specifically designed to detect deviations in the distribution of input features compared to the training data distribution. It continuously monitors incoming inference requests and compares statistical properties (e.g., mean, variance, or histogram) against a baseline computed from the training dataset, alerting when drift is detected.

Answer

SageMaker Clarify

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SageMaker Edge Manager

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SageMaker Model Monitor – Model Quality Monitoring

Question 14

A data engineer is building a data pipeline for a machine learning model that requires both structured and unstructured data. The structured data (customer demographics) is in Amazon RDS, and the unstructured data (customer support chat logs) is in Amazon S3 as JSON files. The engineer needs to combine these datasets into a single training dataset stored in S3 in Parquet format. They must also perform feature engineering such as text vectorization on the chat logs. The pipeline should be serverless and cost-effective. Which approach should they use?

Accepted Answer

Use AWS Glue ETL with a Spark script that reads from RDS (via JDBC) and S3, performs transformations, and writes Parquet.. AWS Glue ETL with a Spark script is the correct choice because it natively supports reading from both Amazon RDS (via JDBC) and Amazon S3 (JSON), performing complex transformations like text vectorization, and writing the output as Parquet. Glue is serverless, cost-effective (pay per DPU-hour), and fully managed, making it ideal for batch ETL pipelines that combine structured and unstructured data for ML training.

Answer

Use a SageMaker Processing job with a custom Python script that reads from both sources and writes to S3.

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Use Amazon Athena to join the data from RDS and S3, then export the results as Parquet.

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Use Amazon Kinesis Data Analytics to read from RDS and S3 and produce a continuous stream of processed data.

Question 15

A data science team uses Amazon SageMaker Model Monitor to detect data drift in production. They notice that the schema of incoming data (number of features) has changed compared to the training baseline. Which type of monitor is BEST suited to detect this issue?

Accepted Answer

Data quality monitor. The Data quality monitor in SageMaker Model Monitor is specifically designed to detect violations in the input data schema, such as changes in the number of features, feature types, or missing values, by comparing incoming data against a baseline computed from the training dataset. Since the issue is a structural change in the schema (number of features), the Data quality monitor is the correct choice.

Answer

Bias drift monitor

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Feature attribution drift monitor

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Model quality monitor

Question 16

A data scientist is preparing a dataset for a binary classification model. The dataset has 10,000 samples, but the positive class represents only 2% of the data. The data scientist needs to train a model that will be evaluated on a hold-out test set that preserves the original class distribution. Which data preparation strategy is MOST appropriate?

Accepted Answer

Oversample the minority class in the training set using SMOTE, and keep the test set as is.. With a 2% positive class, the training set needs class balancing to help the model learn the minority signal, but the hold-out test set must reflect the true 2% prevalence so evaluation metrics (precision, recall, PR-AUC) are realistic. Applying SMOTE only to the training set and leaving the test set untouched satisfies both requirements.

Answer

Undersample the majority class in the training set to match the minority class size.

Answer

Randomly oversample the minority class in the entire dataset and then split.

Answer

Apply SMOTE to the entire dataset before splitting into training and test sets.

Question 17

A data scientist wants to track the lineage of models, datasets, and training jobs in SageMaker. Which SageMaker feature should they use to capture these relationships as artifacts and actions?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking creates a graph of artifacts (datasets, models) and actions (training jobs, endpoints) to track the provenance of ML workflows.

Answer

SageMaker Model Registry

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SageMaker Experiments

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SageMaker Feature Store

Question 18

An ML engineer creates a SageMaker inference pipeline with two containers: a preprocessor and a predictor. The preprocessor is a lightweight Python script that transforms input data. How should the engineer structure the endpoints to ensure both containers run sequentially?

Accepted Answer

Create a PipelineModel in SageMaker with both containers listed in order: first preprocessor, then predictor.. SageMaker's PipelineModel allows you to define an ordered sequence of containers that are executed sequentially within a single HTTPS endpoint. When an inference request is made, the preprocessor container transforms the input, and the output is passed directly to the predictor container, all within the same endpoint invocation. This ensures low latency and tight coupling without needing external orchestration.

Answer

Use batch transform with two transform jobs chained together.

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Use an AWS Lambda function as a proxy to invoke the preprocessor and then the predictor separately.

Answer

Combine the preprocessor and predictor into a single Docker container.

Question 19

A team is fine-tuning a Hugging Face BERT model for text classification using SageMaker. They want to use the Hugging Face estimator for convenience. Which parameter must be set to use a custom training script?

Accepted Answer

entry_point. The entry_point parameter specifies the path to the custom training script that the Hugging Face estimator will execute inside the container. Without it, the estimator has no user-supplied Python file to run, so it cannot perform custom fine-tuning logic. framework_version, instance_type, and hyperparameters configure the environment but do not point to the training code itself.

Answer

framework_version

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instance_type

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hyperparameters

Question 20

A machine learning engineer wants to monitor a deployed model for data drift. Which SageMaker feature should they use to automatically detect drift in the input data distribution compared to the training data baseline?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor can be configured to run monitoring jobs that compare live inference data against a baseline created from training data to detect data drift.

Answer

SageMaker Pipelines

Answer

SageMaker Debugger

Answer

SageMaker Clarify