MLS-C01 Machine Learning Implementation and Operations — Timed 10 Questions

Question 1

A company is using Amazon SageMaker to train a deep learning model. The training job is failing with an error 'CUDA out of memory'. The training instance is an ml.p3.2xlarge with 16 GB GPU memory. The model architecture and batch size are appropriate for this instance size. What is the most likely cause of this error?

Accepted Answer

Enable automatic mixed precision (AMP) training to reduce memory usage.. Option C is correct because enabling automatic mixed precision (AMP) training reduces GPU memory usage by storing tensors in half-precision (FP16) where possible, while keeping critical operations in full precision (FP32). This directly addresses the 'CUDA out of memory' error on an ml.p3.2xlarge instance (16 GB GPU memory) without changing the model architecture or batch size, which are already appropriate.

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Reduce the number of epochs.

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Increase the number of GPUs by using a distributed training instance type.

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Use a smaller instance type to force lower memory usage.

Question 2

A data scientist is deploying a model using Amazon SageMaker. The model endpoint needs to handle real-time inference requests with low latency. The model is a large ensemble of 10 deep learning models, each approximately 500 MB. What is the most cost-effective deployment strategy that meets the low-latency requirement?

Accepted Answer

Use a SageMaker multi-model endpoint to host all models on one or more instances.. A SageMaker multi-model endpoint (MME) allows hosting multiple models on a single or few instances, dynamically loading them from Amazon S3 into memory as needed. This is the most cost-effective option for a large ensemble of 500 MB models because it avoids the expense of separate endpoints or multiple instances per model, while still supporting low-latency real-time inference by keeping frequently used models cached.

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Deploy each model to a separate endpoint and use a load balancer.

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Use a single endpoint with multiple instances behind it.

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Use a SageMaker batch transform job to process inference requests in batches.

Question 3

A company is using Amazon SageMaker to train a model with a custom algorithm. The training script reads data from an S3 bucket using boto3. The training job fails with an 'AccessDenied' error when trying to access the S3 bucket. The IAM role attached to the SageMaker notebook instance has full S3 access. What is the most likely cause?

Accepted Answer

The SageMaker execution role used for the training job does not have S3 access permissions.. The IAM role attached to the SageMaker notebook instance is used for interactive development, but training jobs run under a separate SageMaker execution role. Even if the notebook role has full S3 access, the training job's execution role must also have explicit S3 permissions. The 'AccessDenied' error indicates that the execution role lacks the necessary s3:GetObject or s3:ListBucket actions for the S3 bucket.

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The S3 bucket has a bucket policy that denies access from the SageMaker service.

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The training script is using an incorrect S3 bucket name.

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The SageMaker training job is not configured to use the S3 VPC endpoint.

Question 4

A machine learning engineer is deploying a model using AWS Lambda for real-time inference. The model is a scikit-learn RandomForestClassifier with 100 trees, serialized as a pickle file of 150 MB. The Lambda function has 3 GB memory allocated. However, the inference requests are timing out after 30 seconds. What is the most likely cause?

Accepted Answer

The model is loaded from S3 on every invocation, causing high latency.. Option C is correct because the default behavior of loading a model from S3 on every Lambda invocation introduces significant latency. Each invocation must download the 150 MB pickle file from S3 over the network, deserialize it, and then run inference, which easily exceeds the 30-second timeout. The model should be loaded once outside the handler (in global scope) and reused across invocations to avoid this overhead.

Answer

scikit-learn is not compatible with AWS Lambda.

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The Lambda function does not have enough memory to load the model.

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The Lambda function timeout is set too low; increase it to 5 minutes.

Question 5

A data scientist is using Amazon SageMaker for hyperparameter tuning. The tuning job uses a Bayesian optimization strategy. After 10 training jobs, the objective metric (validation accuracy) has plateaued at 0.85. The data scientist wants to explore more diverse hyperparameter combinations. What should the data scientist do?

Accepted Answer

Increase the exploration weight in the tuning job configuration.. In Bayesian optimization, the exploration weight controls the trade-off between exploring new hyperparameter regions and exploiting known good regions. Increasing this weight encourages the acquisition function to sample more diverse combinations, which can help escape a plateau. Option C is correct because it directly addresses the need for greater diversity in the search space.

Answer

Decrease the exploration weight in the tuning job configuration.

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Switch to random search strategy.

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Increase the number of parallel training jobs.

Question 6

An IAM policy is attached to a SageMaker execution role. A data scientist tries to create a training job using a custom algorithm stored in an ECR repository. The training job fails with an 'AccessDenied' error when pulling the Docker image from ECR. What is the missing permission?

Accepted Answer

ecr:GetDownloadUrlForLayer and ecr:BatchGetImage on the ECR repository. When SageMaker pulls a custom Docker image from ECR during training job creation, the execution role needs permissions to download the image layers. The required actions are ecr:GetDownloadUrlForLayer (to generate pre-signed URLs for each layer) and ecr:BatchGetImage (to retrieve image metadata and layer manifests). Without these, the 'AccessDenied' error occurs because SageMaker cannot authenticate or fetch the container image from the ECR repository.

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ecr:PutImage on the ECR repository

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s3:GetObject on the ECR repository

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sagemaker:CreateTrainingJob on the ECR resource

Question 7

A DevOps engineer created a SageMaker notebook instance using the Terraform configuration shown. The notebook instance is in a VPC with a public subnet. However, the notebook instance cannot access the internet. What is the most likely cause?

Accepted Answer

The subnet does not have a route to an internet gateway.. Option C is correct because a SageMaker notebook instance in a VPC with a public subnet requires a route to an internet gateway (IGW) in the subnet's route table to access the internet. Without that route, traffic from the notebook cannot reach the internet, even if `direct_internet_access` is enabled. The Terraform configuration likely omitted the route to the IGW, causing the connectivity failure.

Answer

The role_arn is incorrect or missing permissions.

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The instance type ml.t2.medium does not support internet access.

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The direct_internet_access parameter is set to 'Enabled' but should be 'Disabled'.

Question 8

A company is using Amazon SageMaker to train a XGBoost model on a large dataset. The training job is taking a long time. The data scientist wants to reduce training time without sacrificing model accuracy. The dataset is 100 GB in CSV format stored in S3. What is the most effective approach?

Accepted Answer

Use Pipe mode to stream data from S3 instead of downloading it first.. Option B is correct because SageMaker's Pipe mode streams data directly from S3 to the training algorithm without writing it to disk, eliminating the I/O bottleneck of downloading the full 100 GB dataset. This reduces training time significantly by overlapping data loading with computation, while preserving model accuracy since the entire dataset is still used.

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Reduce the number of instances to avoid communication overhead.

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Use random sampling to reduce the dataset size to 10 GB.

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Use SageMaker Managed Spot Training to reduce cost, but training time may increase due to interruptions.

Question 9

A company is using Amazon SageMaker to deploy a model for real-time inference. The model endpoint is behind an Application Load Balancer (ALB) for A/B testing. The data scientist notices that the endpoint is returning HTTP 503 errors intermittently. The CloudWatch metrics show that the endpoint's Invocations metric is within limits, but the ModelLatency metric has high variance. What is the most likely cause?

Accepted Answer

The endpoint instance type does not have enough memory for the model.. Option C is correct because high variance in ModelLatency combined with intermittent 503 errors strongly indicates that the model container is running out of memory under load. When memory is insufficient, the inference process may be killed by the kernel (OOM killer) or the container may be throttled, causing sporadic failures that manifest as 503s even though the Invocations metric (request count) appears within limits. The latency spikes occur because the container struggles to allocate memory for each request, leading to timeouts or crashes.

Answer

The model container is using a custom inference code that has a bug.

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The ALB health check is misconfigured and marking instances unhealthy.

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The endpoint is configured with too few instances; increase the instance count.

Question 10

A company is using Amazon SageMaker to train a deep learning model on a large dataset stored in S3. The training job is failing with an OutOfMemory error. The data scientist wants to minimize cost while resolving the issue. Which action should the data scientist take?

Accepted Answer

Change the input mode from 'File' to 'Pipe'.. The OutOfMemory error occurs because the 'File' input mode downloads the entire training dataset to the instance's local storage before training begins, consuming significant memory. Switching to 'Pipe' mode streams data directly from S3 to the training algorithm, reducing memory footprint and avoiding the need for larger instances. This minimizes cost by using the existing instance type while resolving the memory issue.

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Increase the instance type to one with more memory.

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Use the 'auto' setting for the input mode.

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Reduce the batch size hyperparameter.