MLA-C01 Practice Test 46 — 15 Questions

Question 1

Which THREE steps are part of the typical workflow when using SageMaker built-in algorithms?

Accepted Answer

Create a training job. Creating a training job is a fundamental step in the SageMaker workflow for built-in algorithms. You must specify the algorithm, input data location in S3, output path, and compute resources to start model training. Without a training job, no model artifact is generated for deployment.

Answer

Set up a real-time inference endpoint

Answer

Create a custom training image

Question 2

A company is deploying a large model (10GB) for real-time inference. The inference latency is too high. What optimization technique can help?

Accepted Answer

Use SageMaker Neo to compile the model for the target instance. SageMaker Neo compiles the model to optimize it for the target instance hardware, reducing inference latency without sacrificing accuracy. This is especially effective for large models (e.g., 10GB) where runtime performance gains come from hardware-specific optimizations like instruction set tuning and memory access pattern improvements.

Answer

Increase the endpoint's memory allocation

Answer

Switch to a batch transform job

Answer

Reduce the model size by quantization

Question 3

A company uses SageMaker Pipelines to automate model retraining. The pipeline runs daily but sometimes fails due to data quality issues. What is the best design to handle this?

Accepted Answer

Add a data quality check step with Conditional to skip training if data fails.. SageMaker Pipelines supports a data quality check step that can be integrated with a ConditionStep. If the data quality check fails, the ConditionStep can skip the training step entirely, preventing the pipeline from failing due to bad data. This design ensures the pipeline completes successfully (or exits gracefully) without wasting compute resources on training with invalid data.

Answer

Use SageMaker Debugger to monitor training.

Answer

Use SageMaker Model Registry to track model versions.

Answer

Increase the instance size for the training step.

Question 4

A company is deploying multiple models on a single endpoint to reduce costs. They need to update one model without affecting others. Which solution?

Accepted Answer

Use SageMaker Multi-Model Endpoint. SageMaker Multi-Model Endpoint (MME) allows hosting multiple models on a single endpoint, sharing the underlying compute instance. When you need to update one model, you can simply upload a new model artifact (e.g., a new `model.tar.gz`) to Amazon S3, and the endpoint will automatically load the updated version on subsequent inference requests without affecting the other models currently cached or in use.

Answer

Use multiple single-model endpoints behind an Application Load Balancer

Answer

Use SageMaker Batch Transform for some models

Answer

Use a SageMaker Endpoint with multiple production variants

Question 5

A company uses Amazon SageMaker Pipelines for automated retraining. The pipeline includes a processing step that runs a Python script. The script uses the boto3 library to call an AWS service, but the calls are being throttled. What is the MOST effective way to address this within the pipeline?

Accepted Answer

Modify the Python script to include retry with exponential backoff when receiving throttling exceptions.. Implementing retry with exponential backoff directly in the Python script is the most effective way to handle transient throttling exceptions from AWS service API calls. This approach is a best practice for managing service limits within a SageMaker Pipeline processing step, as it allows the script to automatically recover from throttling without modifying the pipeline structure or requiring manual intervention.

Answer

Increase the instance count for the processing step to distribute the API calls.

Answer

Request a service quota increase for the throttling limit.

Answer

Add a wait step in the pipeline before the processing step.

Question 6

Refer to the exhibit. An IAM policy is attached to a user to allow invoking a SageMaker endpoint. A developer tries to call the endpoint from a laptop with IP 203.0.113.5 and receives an access denied error. What is the most likely reason?

Accepted Answer

The condition restricts the IP address to the 10.0.0.0/8 range.. The policy includes a condition that restricts the source IP address to the 10.0.0.0/8 private range. The developer's laptop has a public IP of 203.0.113.5, which does not fall within that range, so the condition fails and access is denied. This is the most likely reason for the error because the condition explicitly blocks requests from outside the specified private network.

Answer

The resource ARN is incorrect.

Answer

The user does not have permission to assume the SageMaker role.

Answer

The policy does not include access to the API action.

Question 7

A data scientist is training a binary classification model using imbalanced data where the positive class is only 1% of the dataset. The scientist wants to maximize the recall for the positive class while maintaining reasonable precision. Which evaluation metric is most appropriate to tune during model selection?

Accepted Answer

F1 score. The F1 score is the harmonic mean of precision and recall, making it ideal for imbalanced datasets where the positive class is only 1%. By tuning the F1 score, the data scientist directly balances the trade-off between maximizing recall (capturing true positives) and maintaining reasonable precision (avoiding false positives), which aligns with the stated goal.

Answer

Log loss

Answer

Area under the ROC curve (AUC)

Answer

Accuracy

Question 8

An ML engineer is setting up monitoring for a SageMaker endpoint. Which THREE metrics should be monitored to detect performance issues? (Select THREE.)

Accepted Answer

Model latency. Model latency is a critical metric for detecting performance issues in a SageMaker endpoint because it directly measures the time taken to process inference requests. High latency can indicate resource bottlenecks, model inefficiency, or scaling problems, and it is essential for meeting service-level agreements (SLAs). Monitoring latency helps identify when the endpoint is underprovisioned or when the model itself has degraded in performance.

Answer

Invocations per second

Answer

DiskWriteBytes

Question 9

A team wants to orchestrate a multi-step ML workflow that includes data preprocessing, hyperparameter tuning, model training, evaluation, and conditional deployment to staging or production based on evaluation metrics. The workflow should run on a schedule and track lineage. Which service should they use?

Accepted Answer

SageMaker Pipelines. SageMaker Pipelines is the correct choice because it is purpose-built for orchestrating multi-step ML workflows, including data preprocessing, hyperparameter tuning, model training, evaluation, and conditional deployment. It natively supports scheduling via EventBridge or a cron expression, tracks lineage automatically through SageMaker Experiments and artifact tracking, and allows conditional branching (e.g., deploy to staging or production based on evaluation metrics) using `ConditionStep`.

Answer

Amazon MWAA (Managed Workflows for Apache Airflow)

Answer

AWS Glue workflows

Answer

AWS Step Functions with Lambda functions for each step

Question 10

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.

Answer

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

Answer

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

Answer

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

Question 11

A data scientist wants to train a binary classification model using Amazon SageMaker. The dataset has 10,000 rows and 50 features. Which SageMaker built-in algorithm is MOST appropriate for this task?

Accepted Answer

XGBoost. XGBoost is a popular algorithm for classification and regression tasks. Linear Learner is more suited for linear models, K-Means is for clustering, and DeepAR is for time series forecasting.

Answer

DeepAR

Answer

K-Means

Answer

Linear Learner

Question 12

A company is deploying a real-time inference endpoint for a natural language processing model using Amazon SageMaker. The model is a fine-tuned BERT variant. The endpoint has been running for two weeks with acceptable latency (average 200 ms). However, over the past 24 hours, the latency has increased to an average of 800 ms, and the number of simultaneous requests has doubled. The team expects traffic to continue to grow. The current endpoint configuration uses a single ml.m5.large instance. The model is loaded into memory once, and the inference framework is PyTorch. The team needs to maintain latency under 500 ms. Which course of action should the team take to address the latency increase while minimizing cost?

Accepted Answer

Enable automatic scaling for the endpoint with a target average latency of 500 ms and use multiple ml.m5.large instances.. The latency increase is caused by a doubling of simultaneous requests overwhelming a single ml.m5.large instance. Enabling automatic scaling with a target average latency of 500 ms allows SageMaker to add more ml.m5.large instances as traffic grows, distributing the load and keeping latency under the threshold. This approach minimizes cost by scaling only when needed, rather than over-provisioning a larger instance.

Answer

Switch to ml.c5.large instances because CPU-optimized instances provide better inference performance for NLP models.

Answer

Increase the instance size to ml.m5.xlarge and keep a single instance.

Answer

Implement a multi-model endpoint with multiple ml.m5.large instances and use Amazon Elastic Inference (EI) accelerators.

Question 13

A data scientist wants to automate retraining of a model weekly and deploy the new model automatically after passing validation. Which AWS service combination is best?

Accepted Answer

SageMaker Pipelines + AWS Step Functions. SageMaker Pipelines orchestrates the ML workflow including training and validation, and Step Functions can trigger deployment. SageMaker alone lacks native scheduling, and Lambda cannot orchestrate complex workflows.

Answer

Amazon EventBridge + SageMaker training job

Answer

Amazon SageMaker Autopilot

Answer

AWS Lambda + SageMaker training job

Question 14

A team wants to track and compare multiple machine learning experiments, including hyperparameters, metrics, and artifacts. They are using Amazon SageMaker. Which AWS service or feature should they use to achieve this?

Accepted Answer

Amazon SageMaker Experiments. Amazon SageMaker Experiments is the correct service because it is specifically designed to track and compare machine learning experiments, including hyperparameters, metrics, and artifacts. It provides a structured way to log, organize, and analyze multiple runs, enabling teams to identify the best-performing model configurations.

Answer

AWS CloudTrail

Answer

Amazon SageMaker Model Registry

Answer

Amazon SageMaker Studio

Question 15

A data engineer needs to join two large datasets from Amazon S3: one containing customer demographics and another containing transaction history. The join key is `customer_id`. To minimize data shuffling and improve performance, the engineer decides to use Amazon SageMaker Processing with Spark. Which configuration should the engineer use?

Accepted Answer

Use a bucketed join with the same number of buckets and co-location. Bucketed joins with the same number of buckets and co-location ensure that data with the same `customer_id` hash is physically stored together on the same nodes. This eliminates the need for expensive shuffles during the join, as Spark can perform the join locally within each executor, dramatically improving performance for large datasets in SageMaker Processing.

Answer

Use a bucketed join with the same number of buckets

Answer

Broadcast join the larger dataset

Answer

Use a repartition on the join key before join