MLS-C01 Machine Learning Implementation and Operations Practice Test 24 — 15 Questions

Question 1

A company is using SageMaker to host a model that performs real-time fraud detection. The model receives high request volumes with occasional spikes. The company wants to ensure that the endpoint can handle spikes without throttling while minimizing cost. Which scaling strategy should be used?

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

Accepted Answer

Use a target tracking scaling policy with a target value of 70% for the SageMakerVariantInvocationsPerInstance metric.. A target tracking scaling policy with the SageMakerVariantInvocationsPerInstance metric is the correct choice because it automatically adjusts the instance count to maintain a target utilization (e.g., 70%), handling spikes without manual intervention while minimizing cost by scaling down during low traffic. This is the recommended approach for real-time endpoints with variable traffic, as it aligns with AWS best practices for dynamic scaling.

Answer

Use a simple scaling policy with a step adjustment based on the InvocationsPerInstance metric.

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Manually adjust the instance count based on monitoring dashboards.

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Use a scheduled scaling action to add instances during peak hours.

Question 2

A data scientist is using SageMaker to train a model using the built-in XGBoost algorithm. The training job fails with the error 'AlgorithmError: Framework error: No module named 'xgboost''. What is the most likely cause?

Accepted Answer

The training job is using a custom container that does not have XGBoost installed.. The error 'No module named xgboost' indicates the Python environment inside the training container cannot import the XGBoost library. When using SageMaker's built-in XGBoost algorithm, the correct algorithm container already includes XGBoost; if the job is pointed at a custom container (or a generic framework container) without XGBoost installed, this import error occurs. The most likely cause is that the training job is using a custom container lacking XGBoost.

Answer

The training data is not in CSV format.

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The IAM role does not have permission to access SageMaker.

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The S3 output path is incorrect.

Question 3

A data scientist is using SageMaker to train a model and wants to track experiments, including hyperparameters and metrics. Which TWO actions should the scientist take to set up experiment tracking? (Choose TWO.)

Accepted Answer

Use the SageMaker Experiments Python SDK to create an experiment and log runs.. The SageMaker Experiments Python SDK provides the primary interface for creating and managing experiments, allowing the data scientist to log runs, hyperparameters, and metrics in a structured way. This SDK directly integrates with SageMaker training jobs and notebook executions to capture experiment metadata.

Answer

Enable SageMaker Model Monitor to track training metrics.

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Configure CloudWatch Logs to store experiment data.

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Enable SageMaker Studio to automatically capture experiments.

Question 4

A data scientist is training a model using SageMaker and wants to use spot instances to reduce costs. Which THREE considerations should the scientist evaluate? (Choose THREE.)

Accepted Answer

The training job must support checkpointing to save progress.. SageMaker managed spot training requires checkpointing to save model state at regular intervals. If a spot instance is interrupted, the training job can resume from the last checkpoint rather than starting from scratch, which is essential for long-running or expensive training jobs.

Answer

Spot instances have a fixed, lower price than on-demand.

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Spot instances are only available for inference, not training.

Question 5

A data scientist wants to deploy a PyTorch model for real-time inference with latency under 100 ms. Which AWS service is most suitable?

Accepted Answer

Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints are designed for low-latency inference, typically under 100 ms, by hosting a model behind an HTTPS endpoint that auto-scales based on traffic. They support PyTorch natively via pre-built containers or custom containers, making them the most suitable choice for this requirement.

Answer

Amazon SageMaker Processing

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AWS Lambda with container image

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Amazon SageMaker Batch Transform

Question 6

A data scientist needs to run a one-time SQL query on a large dataset in S3 to create a training dataset. The query involves aggregations and joins. Which service is most suitable?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless service that allows running SQL queries directly on data stored in S3, ideal for one-time ad-hoc queries with aggregations and joins. Option A (AWS Glue ETL) is designed for scheduled ETL jobs, not ad-hoc queries. Option C (Amazon EMR with Spark SQL) provides powerful processing but is overkill and requires cluster management. Option D (Amazon RDS) would require moving data into a database, which is inefficient.

Answer

AWS Glue ETL job

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Amazon EMR with Spark SQL

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Amazon RDS with data loaded into it

Question 7

A data scientist needs to store and version machine learning models, along with metadata such as hyperparameters and metrics. Which AWS service is designed for this purpose?

Accepted Answer

Amazon SageMaker Model Registry. Amazon SageMaker Model Registry is purpose-built for cataloging, versioning, and managing machine learning models. It stores model artifacts, metadata such as hyperparameters and metrics, and supports approval workflows for deployment. This directly matches the requirement to store and version models with associated metadata.

Answer

Amazon S3 with versioning enabled

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Amazon DynamoDB

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Amazon Elastic Container Registry (ECR)

Question 8

A company is using SageMaker to train a linear learner algorithm. The training log shows that the algorithm converges but the final loss is still high. Which change is most likely to improve the model?

Accepted Answer

Add feature crosses or polynomial features. A high final loss despite convergence indicates that the model is underfitting — the linear decision boundary is too simple to capture the underlying patterns in the data. Adding feature crosses or polynomial features increases the model's expressiveness by introducing non-linear interactions, allowing the linear learner to fit more complex relationships and reduce the loss.

Answer

Reduce the early stopping tolerance

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Increase the maximum runtime

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Increase the number of training instances

Question 9

A company wants to automate the retraining of a model weekly using new data. The training script is in a SageMaker notebook. Which implementation is most maintainable?

Accepted Answer

Convert the notebook to a Python script, create a Docker container, and use SageMaker Pipelines with a schedule. It transforms the notebook into a production-grade, containerized training pipeline that can be scheduled natively via SageMaker Pipelines. This approach decouples the training logic from the notebook environment, ensures reproducibility through Docker, and leverages SageMaker's managed infrastructure for automated retraining without manual intervention.

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Set up a cron job on an EC2 instance to run the training script

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Schedule the notebook to run via a SageMaker Lifecycle Configuration script

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Use AWS CloudFormation to provision a training job on a schedule

Question 10

A data scientist needs to select a model training infrastructure that supports distributed training across multiple GPUs and provides automatic model parallelism. Which TWO AWS services should the scientist consider?

Accepted Answer

Amazon EMR. Amazon EMR is correct because it supports distributed training across multiple GPUs using frameworks like TensorFlow, PyTorch, and Apache Spark, and it can automatically handle model parallelism through its integration with Horovod or custom distributed training scripts. Amazon SageMaker is correct because it provides built-in distributed training libraries (e.g., SageMaker Distributed Data Parallel and Model Parallel) that automatically partition model layers across multiple GPUs, enabling efficient training of large models.

Answer

AWS Glue

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AWS Lambda

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Amazon Redshift

Question 11

A training job log shows this error. The training instance is an ml.m5.large with 8 GB EBS storage. The training data is 500 MB, and the model size is expected to be 200 MB. What is the most likely cause?

Accepted Answer

The training process is generating large temporary files that fill the instance's local storage. The training job runs on an ml.m5.large instance with only 8 GB of EBS storage. Given that the training data is 500 MB and the model size is 200 MB, there is ample space for those, but the error indicates a lack of storage space. This is most likely caused by temporary files (e.g., checkpoints, logs, or intermediate data) generated during training that fill up the remaining storage. Option A is incorrect because a download issue would show errors like 'Download failed' or 'NoSuchKey', not a storage error. Option B is incorrect because S3 permission issues would result in 'AccessDenied' errors. Option C is incorrect because insufficient RAM would cause a 'MemoryError' or out-of-memory kill, not a storage space error.

Answer

The training data is not fully downloaded from S3 before processing

Answer

The S3 bucket does not have write permissions

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The training instance does not have enough RAM

Question 12

A company is using Amazon SageMaker to train and deploy a fraud detection model. The model is a gradient boosting machine (GBM) trained on a dataset with 10 million rows and 50 features. The training job runs on an ml.m5.2xlarge instance with 8 vCPUs and 32 GB memory. The training completes successfully, and the model is deployed to a real-time endpoint. After deployment, the inference latency is around 200 ms per request, which is acceptable. However, after a week, the company observes that latency increases to over 1 second during peak hours (12:00-13:00 UTC). CloudWatch metrics show CPU utilization on the endpoint instance reaches 95% during these peaks. The endpoint is configured with a single ml.m5.large instance. The company wants to maintain latency under 500 ms during peak hours without incurring unnecessary cost during off-peak hours. Which solution should the company implement?

Accepted Answer

Configure SageMaker endpoint auto scaling with a target CPU utilization of 70% and a minimum instance count of 1. Configuring auto scaling based on CPU utilization dynamically adds instances during peak hours (when CPU exceeds target) and removes them during off-peak, maintaining latency under 500 ms while minimizing cost. Option A is incorrect because reducing instances to zero and manually launching a new endpoint is not automated, causes downtime, and is impractical. Option B is incorrect because Batch Transform is designed for offline, batch inference, not for real-time requests. Option D is incorrect because replacing the instance with a larger, always-on type (ml.m5.4xlarge) would handle peak load but incur higher cost during off-peak hours, unlike auto scaling which scales in.

Answer

Reduce the number of instances to zero during off-peak hours and manually launch a new endpoint every day at 12:00

Answer

Switch to SageMaker Batch Transform and have the application send requests in batches

Answer

Replace the endpoint instance type with ml.m5.4xlarge to handle peak load

Question 13

A data scientist is training a TensorFlow model on a single GPU instance. The training is taking too long. Which AWS service should be used to reduce training time by distributing the workload across multiple GPUs?

Accepted Answer

Amazon SageMaker. Amazon SageMaker provides built-in support for distributed training across multiple GPUs using its managed training infrastructure. By configuring a SageMaker training job with a 'distributed training' strategy (e.g., SageMaker's distributed data parallelism library), the TensorFlow model can automatically split the workload across multiple GPU instances, significantly reducing training time. SageMaker handles the underlying cluster orchestration, network setup, and fault tolerance, making it the correct choice for this scenario.

Answer

AWS Glue

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Amazon EMR

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AWS Batch

Question 14

A machine learning team is using Amazon SageMaker to train a model with a custom algorithm packaged in a Docker container. The training job fails with the error 'Error: Unable to locate sagemaker-training toolkit.' What is the MOST likely cause?

Accepted Answer

The container does not include the SageMaker Training Toolkit. The SageMaker Training Toolkit is a required library that provides the interface between SageMaker and custom Docker containers for training jobs. When a custom container does not include this toolkit, SageMaker cannot execute the training script or communicate with the container, resulting in the 'Unable to locate sagemaker-training toolkit' error. This is a mandatory dependency for any custom training container used with SageMaker.

Answer

The container does not have internet access to download dependencies

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The instance type is incompatible with the container

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The training role does not have permissions to access the container repository

Question 15

A data scientist needs to perform hyperparameter optimization for a model. Which AWS service provides built-in hyperparameter tuning jobs?

Accepted Answer

Amazon SageMaker. Amazon SageMaker provides built-in hyperparameter tuning jobs as a managed service, allowing data scientists to automatically search for optimal hyperparameter values using strategies like Bayesian optimization, random search, or Hyperband. This is a core feature of SageMaker's automatic model tuning capability, which integrates directly with SageMaker training jobs and supports early stopping to reduce compute costs.

Answer

Amazon EMR

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AWS Step Functions

Answer

AWS Batch