MLS-C01 Machine Learning Implementation and Operations Practice Test 8 — 15 Questions

Question 1

An ML engineer needs to run a hyperparameter tuning job on Amazon SageMaker. The training algorithm supports distributed training across multiple GPUs. The engineer wants to minimize the total time to find the best hyperparameters. Which strategy should be used?

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

Accepted Answer

Use Bayesian optimization as the tuning strategy.. Bayesian optimization is the correct choice because it builds a probabilistic model of the objective function and uses an acquisition function to select the most promising hyperparameters to evaluate next. This approach converges to optimal hyperparameters in far fewer trials than random or grid search, minimizing total tuning time. SageMaker's built-in hyperparameter tuning jobs natively support Bayesian optimization and can leverage distributed training across multiple GPUs without any additional configuration.

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Use random search to explore a wide range.

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Use grid search to cover all combinations.

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Use Hyperband which is designed for distributed training.

Question 2

A company is deploying a machine learning model for real-time fraud detection using Amazon SageMaker. The model must have a p99 inference latency under 50ms. Which TWO actions should the ML team take to meet the latency requirement?

Accepted Answer

Use SageMaker Neo to compile and optimize the model for the target instance type.. SageMaker Neo compiles and optimizes trained models for specific hardware targets, reducing inference latency by up to 2x without sacrificing accuracy. By applying hardware-specific optimizations such as kernel fusion and memory layout tuning, Neo ensures the model runs efficiently on the chosen instance type, directly helping to achieve sub-50ms p99 latency.

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Use a multi-model endpoint to reduce cold starts.

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Use SageMaker Batch Transform for near-real-time inference.

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Configure automatic scaling to add instances based on CPU utilization.

Question 3

A machine learning team is building a real-time inference pipeline using Amazon SageMaker. The team has multiple models that need to be served, but usage patterns are unpredictable and traffic spikes occur several times a day. The team wants to minimize costs while maintaining low latency. Which THREE actions should the team take?

Accepted Answer

Use SageMaker inference with Spot Instances to reduce cost.. Using Spot Instances for SageMaker inference can significantly reduce costs (up to 60-90% compared to On-Demand) while still providing the compute needed for real-time inference. Spot Instances are suitable when the workload can tolerate interruptions, and with SageMaker's managed Spot support, the endpoint can automatically fall back to On-Demand capacity if Spot capacity is reclaimed, ensuring availability during traffic spikes.

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Enable provisioned concurrency on the endpoint to reduce cold starts.

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Use SageMaker Batch Transform for all inference requests.

Question 4

A data scientist is using Amazon SageMaker to train a large neural network on a GPU instance. The training is taking longer than expected. The scientist wants to reduce training time without changing the model architecture. Which TWO approaches should the scientist consider?

Accepted Answer

Use SageMaker's distributed training with multiple GPU instances.. SageMaker's distributed training can split the large neural network across multiple GPU instances, reducing wall-clock training time through data parallelism or model parallelism. Option D is correct because switching to a larger GPU instance type with more CUDA cores increases the computational throughput per step, directly accelerating training without altering the model architecture.

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Use SageMaker Automatic Model Tuning to find optimal hyperparameters.

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Use SageMaker Managed Spot Training to reduce cost.

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Enable SageMaker Debugger to capture training metrics.

Question 5

An ML engineer is deploying a model on a SageMaker endpoint and wants to ensure that only authorized users and services can invoke the endpoint. The company uses AWS IAM for access control and requires that the endpoint be invoked only from within a specific VPC. What combination of actions should the engineer take? (Choose the single best answer.)

Accepted Answer

Create a VPC endpoint for Amazon SageMaker and attach a policy that only allows invocation from the VPC. Use IAM roles to restrict which users can invoke the endpoint.. It combines a VPC endpoint for SageMaker with an IAM policy to restrict invocation to authorized users and traffic originating from the specified VPC. A VPC endpoint (interface endpoint) uses AWS PrivateLink to allow private connectivity between the VPC and SageMaker without traversing the public internet, and attaching a resource-based policy to the endpoint ensures only requests from within the VPC are accepted. IAM roles then enforce user-level authorization, meeting both the VPC-only and IAM access control requirements.

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Use AWS CloudFront to restrict access based on IP addresses.

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Use API Gateway in front of the SageMaker endpoint and attach a resource policy to API Gateway.

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Configure network ACLs on the VPC subnet to allow only the endpoint's security group.

Question 6

A company uses Amazon SageMaker to train a model using a custom Docker container. The training job fails with an error: "Unable to write to /opt/ml/output/data". The data scientist checks the container and finds that the /opt/ml directory is not writable. What is the MOST likely cause?

Accepted Answer

The container runs as a non-root user that lacks write permissions to /opt/ml.. The error 'Unable to write to /opt/ml/output/data' indicates a permission issue. By default, SageMaker training containers run as a non-root user (uid 1000) for security reasons. If the Docker image is built with /opt/ml owned by root and without world-writable permissions, the non-root user cannot write to that directory, causing the failure.

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The Docker image is built from a base image that does not have the required libraries.

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The SageMaker training job is configured with insufficient memory.

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The training script is not copying the model to /opt/ml/model.

Question 7

An ML team wants to perform batch inference on a large dataset stored in Amazon S3 using a pre-trained model. The team needs to process the data in parallel across multiple instances to reduce processing time. Which approach should they use?

Accepted Answer

Use SageMaker Batch Transform with multiple instances.. SageMaker Batch Transform is designed specifically for batch inference on large datasets stored in Amazon S3. It automatically distributes the data across multiple instances, processes them in parallel, and writes the results back to S3, making it the optimal choice for reducing processing time.

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Use SageMaker Processing to run a custom inference script.

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Use SageMaker Training to run inference as a training job.

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Use SageMaker Ground Truth to process the data.

Question 8

Refer to the exhibit. An ML engineer attaches this IAM policy to a user. The user wants to invoke the SageMaker endpoint my-endpoint from an EC2 instance with public IP 52.1.1.1. What will happen?

Accepted Answer

The invocation is allowed because the source IP is not in the denied ranges.. The IAM policy explicitly allows the `sagemaker:InvokeEndpoint` action, and the `Deny` statement only denies requests from IP addresses in the ranges 10.0.0.0/8 or 192.168.0.0/16. Since the EC2 instance has a public IP of 52.1.1.1, which is not within those denied ranges, the invocation is allowed. The policy does not require the user to be in a VPC or to have endpoint creation permissions for invoking an existing endpoint.

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The invocation fails because the user does not have permission to create an endpoint.

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The invocation is denied because the Deny statement applies to all resources.

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The invocation is denied because the user is not in a VPC.

Question 9

Refer to the exhibit. A data scientist is deploying a PyTorch model on a SageMaker endpoint. When the endpoint is invoked, the above error appears in CloudWatch logs. What is the MOST likely cause?

Accepted Answer

The model artifact was not properly saved or is missing from the S3 location.. The error shown in CloudWatch logs is a `FileNotFoundError` or `No such file or directory` when SageMaker attempts to load the model artifact. This indicates that the model file (e.g., `model.pth` or `model.pt`) is missing from the specified S3 bucket path or was not properly packaged during training. SageMaker endpoints require the model artifact to be present and correctly referenced in the `model_data_url` parameter; otherwise, the container fails to load the model and throws this error.

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The endpoint instance type does not support the required CUDA version.

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The endpoint instance does not have enough memory to load the model.

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The input tensor shape does not match the model's expected input shape.

Question 10

Refer to the exhibit. An ML engineer creates a CloudFormation stack with this template. The stack creation succeeds, but when the engineer tries to invoke the endpoint, it returns a ModelError. The CloudWatch logs show that the container exited with error. What is the MOST likely cause?

Accepted Answer

The execution role does not have permissions to pull the Docker image from ECR.. The CloudFormation template likely does not grant the SageMaker execution role the necessary `ecr:GetDownloadUrlForLayer` and `ecr:BatchGetImage` permissions to pull the container image from Amazon ECR. Without these permissions, the SageMaker service cannot download the Docker image to the ML compute instances, causing the container to fail with a ModelError and exit error in CloudWatch logs.

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The initial instance count is set to 2, which is insufficient for the model size.

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The endpoint is not deployed in a VPC and cannot access the S3 bucket.

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The EndpointConfig references the model but the model is not yet created.

Question 11

A financial services company is deploying a machine learning model for credit risk assessment. The model must have an inference latency under 200ms and must be able to handle up to 1000 transactions per second (TPS). The company wants to minimize costs. The model is a gradient boosting model implemented in XGBoost. Which SageMaker deployment option should the team choose?

Accepted Answer

Deploy the model on a SageMaker real-time endpoint with multiple instances behind a load balancer.. A SageMaker real-time endpoint with multiple instances behind a load balancer provides the sub-200ms inference latency and 1000 TPS throughput required for synchronous, low-latency predictions. XGBoost models are well-suited for real-time endpoints, and horizontal scaling with multiple instances ensures high availability and cost efficiency by matching capacity to demand.

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Use SageMaker Batch Transform to process transactions in batches.

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Use SageMaker asynchronous inference for queued requests.

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Use SageMaker Serverless Inference for automatic scaling.

Question 12

An ML team uses Amazon SageMaker to train a deep learning model. The training job runs on a single ml.p3.2xlarge instance and is taking 10 hours. The team wants to reduce the training time to under 2 hours without changing the model architecture. Which approach is MOST effective?

Accepted Answer

Use SageMaker distributed training with multiple ml.p3.2xlarge instances.. A is correct because SageMaker's distributed training framework can partition the training workload across multiple ml.p3.2xlarge instances, each with one NVIDIA V100 GPU, enabling data parallelism that scales near-linearly. With sufficient instances (e.g., 5 or more), the 10-hour job can be reduced to under 2 hours without altering the model architecture, as the framework handles gradient synchronization via AllReduce.

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Use SageMaker Managed Spot Training to reduce cost.

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Switch to a single ml.p3.16xlarge instance with more GPUs.

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Enable SageMaker Debugger to identify bottlenecks.

Question 13

A company wants to use Amazon SageMaker to host a model that was trained using a custom algorithm. The model artifact is stored in Amazon S3. The company wants to ensure that the endpoint can automatically scale based on the number of incoming requests. Which configuration should the company use?

Accepted Answer

Create a SageMaker real-time endpoint and configure automatic scaling using a target tracking policy.. A SageMaker real-time endpoint with automatic scaling using a target tracking policy allows the endpoint to dynamically adjust the number of instances based on the incoming request load. This configuration is ideal for hosting a custom algorithm model artifact stored in S3, as it provides low-latency inference and can scale out or in based on a target metric like average CPU utilization or request count per instance.

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Create a SageMaker multi-model endpoint with automatic scaling.

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Use SageMaker Serverless Inference which scales automatically.

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Use SageMaker Batch Transform with a scheduled job.

Question 14

A company uses Amazon SageMaker to train a model. The training job fails with an 'OutOfMemory' error. The training data is stored in S3 and the instance type is ml.m5.xlarge. What is the most efficient way to resolve this issue?

Accepted Answer

Use a larger instance type, such as ml.m5.2xlarge. The 'OutOfMemory' error indicates that the ml.m5.xlarge instance (4 vCPUs, 16 GiB memory) does not have enough RAM to hold the training data and model during processing. Upgrading to ml.m5.2xlarge (8 vCPUs, 32 GiB memory) directly increases available memory, resolving the issue without requiring code changes or architectural modifications. This is the most efficient solution because it requires no script alterations and leverages SageMaker's built-in instance scaling.

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Enable managed spot training

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Reduce the batch size in the training script

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Increase the number of instances using distributed training

Question 15

A data scientist is deploying a real-time inference endpoint using SageMaker. The model is a large NLP model requiring GPU for low latency. The endpoint must be highly available across two Availability Zones. Which deployment configuration meets these requirements?

Accepted Answer

Deploy a multi-model endpoint on an ml.p3.2xlarge instance with auto-scaling and at least two instances in different AZs. It uses a GPU instance (ml.p3.2xlarge) to meet the low-latency requirement for a large NLP model, and it deploys at least two instances across different Availability Zones (AZs) to achieve high availability. SageMaker multi-model endpoints allow hosting multiple models on the same endpoint, but here the key is the instance type and the multi-instance, multi-AZ deployment for fault tolerance.

Answer

Deploy a single model endpoint on an ml.c5.xlarge instance with auto-scaling

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Use SageMaker batch transform on GPU instances

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Deploy a single model endpoint on an ml.p3.2xlarge instance with one instance