MLS-C01 Machine Learning Implementation and Operations Practice Test 16 — 15 Questions

Question 1

A machine learning engineer needs to deploy a TensorFlow model to a SageMaker endpoint. The model expects a specific input format. The engineer has the model artifacts stored in an S3 bucket. Which step is REQUIRED to deploy the model?

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

Accepted Answer

Create a SageMaker Model object using the TensorFlow serving image.. To deploy a TensorFlow model to a SageMaker endpoint, you must create a SageMaker Model object that references the model artifacts in S3 and specifies the appropriate TensorFlow Serving container image. This image handles loading the model and exposing a RESTful or gRPC inference endpoint. Without this step, SageMaker cannot associate the artifacts with a serving container to start the endpoint.

Answer

Register the model in SageMaker Model Registry.

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Create a SageMaker training job to re-train the model.

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Save the model as a SavedModel format.

Question 2

A company is building a recommendation system using Amazon SageMaker. The data is stored in a large S3 bucket with millions of small CSV files. The team wants to train a factorization machines model. Which data ingestion strategy will be MOST efficient?

Accepted Answer

Use a SageMaker Processing job with a Spark container to read the files and write a single RecordIO file.. SageMaker's factorization machines algorithm requires data in RecordIO-wrapped protobuf format for optimal performance, especially with high-dimensional sparse data. Using a SageMaker Processing job with Spark efficiently reads millions of small CSV files from S3, coalesces them into a single or few large RecordIO files, and avoids the overhead of many small S3 GET requests during training, which would otherwise cause severe I/O bottlenecks.

Answer

Use Amazon Athena to query the data and output to a single CSV.

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Point the training job directly to the S3 bucket containing the CSV files.

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Use SageMaker Data Wrangler to create a data flow and export to a training dataset.

Question 3

An ML team is using SageMaker Autopilot to automatically build a binary classification model. The dataset has 500,000 rows and 200 columns, with a severe class imbalance (1% positive). Which configuration should the team set to address the imbalance?

Accepted Answer

Specify the 'objective' as 'F1' or 'AUC' to optimize for imbalanced data.. SageMaker Autopilot allows specifying the objective metric for optimization. For imbalanced datasets, metrics like F1 score or AUC are more appropriate than accuracy because they account for precision and recall or the trade-off between true positive and false positive rates. By setting the objective to 'F1' or 'AUC', Autopilot will optimize the model for these metrics, which better handle class imbalance. Option B (MulticlassClassification) is for multi-class problems, not binary imbalance; Option C (SMOTE) is not supported by Autopilot; Option D (stratified splitting) helps ensure representative validation splits but does not directly address the imbalance in model optimization.

Answer

Set the 'problem_type' to 'MulticlassClassification' to handle imbalance.

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Use the 'AutoML' job with 'EnsembleMode' and 'SMOTE' sampling.

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Configure the data split to use stratified sampling based on the target.

Question 4

A company wants to use Amazon Rekognition to detect objects in images stored in an S3 bucket. The images are uploaded by users. Which IAM policy statement is necessary to allow Rekognition to read from the bucket?

Accepted Answer

s3:GetObject. Amazon Rekognition needs to read the image files from the S3 bucket to perform object detection. The s3:GetObject permission grants read access to the objects stored in the bucket, which is the minimum required action for Rekognition to retrieve and analyze the images.

Answer

s3:PutObject

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s3:DeleteObject

Answer

s3:ListBucket

Question 5

A data scientist is using SageMaker to train a deep learning model. The training script uses TensorFlow and runs on a single p3.2xlarge instance. The scientist wants to reduce training time by using multiple GPUs. What should the scientist do?

Accepted Answer

Modify the training script to use Horovod for distributed training.. Horovod is a distributed deep learning framework that integrates with TensorFlow to enable multi-GPU training across multiple instances. By modifying the training script to use Horovod's `hvd.DistributedOptimizer` and broadcasting initial variables, the data scientist can leverage multiple GPUs on a single p3.2xlarge instance (which has 1 GPU) or scale to multiple instances, directly reducing training time through data parallelism.

Answer

Increase the instance count to 4 without changing the script.

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Switch to PyTorch framework.

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Use SageMaker Managed Spot Training.

Question 6

A team has deployed a SageMaker endpoint for a sentiment analysis model. The model was trained on text data from social media. After deployment, the team notices that the model's accuracy has dropped significantly after 3 months. Which action should the team take to detect and address this issue?

Accepted Answer

Enable SageMaker Model Monitor to detect data drift and trigger a retraining pipeline.. B is correct because SageMaker Model Monitor is specifically designed to detect data drift (changes in the input data distribution over time) and model drift (degradation in prediction quality). When a sentiment analysis model trained on social media text sees a drop in accuracy after months, it is likely due to shifts in language, slang, or topics. Model Monitor can continuously track the distribution of input features and predictions against a baseline, and when drift is detected, it can automatically trigger a retraining pipeline to update the model, directly addressing the root cause of the accuracy drop.

Answer

Use SageMaker A/B testing to compare with a new model.

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Re-deploy the model using the same training script.

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Create a CloudWatch alarm on invocation errors.

Question 7

Which TWO actions can reduce inference latency for a SageMaker real-time endpoint? (Choose 2.)

Accepted Answer

Choose a larger instance type with more compute capacity.. Choosing a larger instance type with more compute capacity (Option A) reduces inference latency because it provides more CPU/GPU and memory resources, enabling the model to process each request faster. This directly decreases the time per inference, which is the primary driver of latency for real-time endpoints.

Answer

Add more instances behind the endpoint.

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Use batch transform instead.

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Switch to asynchronous inference.

Question 8

Which THREE are valid considerations when deploying a large deep learning model (10 GB) on a SageMaker endpoint? (Choose 3.)

Accepted Answer

Enable SageMaker Data Compression for network transfer.. SageMaker Data Compression uses HTTP compression (e.g., gzip) to reduce the payload size during network transfer between the client and endpoint, which is critical for a 10 GB model to minimize latency and bandwidth consumption. This is especially beneficial when the model is large and inference requests involve substantial input or output data.

Answer

Use SageMaker Serverless Inference to avoid managing instances.

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Attach Elastic Inference accelerators.

Question 9

Which TWO SageMaker features can be used to monitor and debug training jobs? (Choose 2.)

Accepted Answer

SageMaker Debugger. SageMaker Debugger (A) is correct because it provides real-time monitoring and debugging of training jobs by capturing tensors, gradients, and other metrics during training, allowing you to detect issues like vanishing gradients or overfitting. Amazon CloudWatch Logs (D) is correct because it automatically collects and stores logs from SageMaker training jobs, including algorithm output and framework logs, which you can monitor for errors or anomalies.

Answer

SageMaker Model Monitor

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SageMaker Ground Truth

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SageMaker Clarify

Question 10

Refer to the exhibit. A developer has this IAM policy attached to an IAM role used by SageMaker. When attempting to create an endpoint, the operation fails with an access denied error. What is the MOST likely cause?

Accepted Answer

The policy is missing s3:ListBucket on the model bucket.. The error occurs because SageMaker needs to list objects in the S3 bucket where the model artifacts are stored before it can download them to create the endpoint. The attached policy grants s3:GetObject but not s3:ListBucket, which is required for the initial validation and listing of model artifacts in the bucket. Without s3:ListBucket, the CreateEndpoint API call fails with an access denied error.

Answer

The policy is missing ecr:DescribeRepositories.

Answer

The policy is missing sagemaker:DescribeEndpoint.

Answer

The policy is missing sagemaker:InvokeEndpoint.

Question 11

Refer to the exhibit. A data scientist is training a PyTorch model on a SageMaker ml.p3.2xlarge instance (16 GB GPU memory). The training fails with the shown error. Which change should the scientist make to resolve the error?

Accepted Answer

Reduce the batch size in the training script.. The error is an out-of-memory (OOM) condition on the GPU. Reducing the batch size directly decreases the memory footprint per training step, allowing the model to fit within the 16 GB GPU memory of the ml.p3.2xlarge instance. This is the most immediate and effective fix for a GPU memory exhaustion error in PyTorch.

Answer

Increase the number of instances to 2.

Answer

Use SageMaker Managed Spot Training.

Answer

Increase the number of epochs.

Question 12

Refer to the exhibit. An administrator has attached this IAM policy to a user. The user tries to start a SageMaker training job that uses a custom Docker image from Amazon ECR. The training job fails with an access denied error. What is the MOST likely reason?

Accepted Answer

The policy is missing ecr:GetDownloadUrlForLayer and ecr:BatchGetImage.. The IAM policy grants sagemaker:* and s3:* actions but does not include the specific ECR permissions required to pull a custom Docker image. When SageMaker launches a training job with a custom image, it needs ecr:GetDownloadUrlForLayer and ecr:BatchGetImage to authenticate and download the image layers from Amazon ECR. Without these permissions, the training job fails with an access denied error.

Answer

The s3:* action is too permissive and should be scoped.

Answer

The iam:PassRole permission is missing the SageMaker service principal.

Answer

The sagemaker:* action should be restricted to specific resources.

Question 13

A data scientist is training a model using Amazon SageMaker and notices that training is taking much longer than expected. The training job uses a single ml.p3.2xlarge instance. The data is stored in S3 and is about 50 GB in size. Which action would MOST likely reduce training time?

Accepted Answer

Change the input mode from File to Pipe.. Changing the input mode from File to Pipe reduces training time by streaming data directly from S3 to the training algorithm without first downloading it to the local disk. In File mode, SageMaker first downloads the entire 50 GB dataset to the instance's Amazon EBS volume, which adds significant I/O latency and storage overhead. Pipe mode uses a Linux FIFO (named pipe) to feed data on-the-fly, eliminating the download step and allowing the GPU to start processing sooner.

Answer

Enable automatic data sharding in the SageMaker training job.

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Enable S3 server-side encryption on the training data.

Answer

Use a larger instance type, such as ml.p3.16xlarge.

Question 14

A machine learning engineer is deploying a model using Amazon SageMaker. The model is a PyTorch model that performs real-time inference with low latency requirements. The engineer wants to use automatic scaling based on the number of concurrent requests. Which SageMaker feature should be used to achieve this?

Accepted Answer

Use Amazon SageMaker automatic scaling with a target tracking scaling policy.. Amazon SageMaker automatic scaling with a target tracking scaling policy is the correct feature because it allows the endpoint to dynamically adjust the number of instances based on a predefined metric, such as the number of concurrent requests (e.g., using the SageMakerVariantInvocationsPerInstance metric). This directly meets the requirement for automatic scaling based on concurrent requests while maintaining low latency for real-time PyTorch inference.

Answer

Create an AWS Auto Scaling group for the SageMaker endpoint.

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Enable Elastic Load Balancing for the endpoint.

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Deploy the model behind Amazon API Gateway with a Lambda function.

Question 15

A company is using Amazon SageMaker to build a binary classification model. The dataset is highly imbalanced, with 95% negative class and 5% positive class. Which technique should be used to address the class imbalance?

Accepted Answer

Use a weighted loss function during training.. Using a weighted loss function (e.g., class weights in SageMaker's built-in XGBoost or custom PyTorch loss) assigns a higher penalty to misclassifications of the minority positive class. This directly addresses the 95:5 imbalance by making the model more sensitive to the positive class during gradient updates, without discarding data.

Answer

Use accuracy as the primary evaluation metric.

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Perform random under-sampling of the majority class.

Answer

Remove all examples from the majority class.