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

Question 1

A SageMaker endpoint configuration is shown in the exhibit. The company wants to deploy the model to a real-time endpoint. What is missing from this configuration to successfully create the endpoint?

Accepted Answer

The endpoint name is not specified in the configuration. The endpoint configuration must include an EndpointName parameter to uniquely identify the endpoint within the AWS account and region. Without it, the CreateEndpoint API call fails because the service cannot route traffic or manage the deployment. The exhibit shows a valid EndpointConfigName, but the endpoint itself is not named, which is a required field for real-time inference endpoints.

Answer

The model name is missing

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The initial instance count is missing

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The accelerator type is missing

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The data capture configuration is missing

Question 2

A data scientist is using Amazon SageMaker to train a model. Training is taking longer than expected. The scientist notices that the training job is using a single instance type with limited GPU memory. Which action will MOST likely reduce training time?

Accepted Answer

Configure the training job to use distributed data parallelism across multiple instances.. The training job is bottlenecked by limited GPU memory on a single instance. Distributed data parallelism splits the dataset across multiple instances, each processing a subset of the data in parallel, which directly reduces wall-clock training time by leveraging aggregate GPU memory and compute. This is the most effective action to address the stated problem of slow training due to limited GPU memory.

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Use SageMaker Managed Spot Training to lower cost.

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Use batch normalization layers.

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Enable SageMaker Debugger for real-time monitoring.

Question 3

An ML team deploys a real-time inference endpoint on Amazon SageMaker. Users report high latency. The model is a PyTorch model using a custom container. Which combination of changes should the team implement to reduce latency? (Choose the best answer.)

Accepted Answer

Use SageMaker Inference Recommender to benchmark different instance families and select the best.. SageMaker Inference Recommender runs load tests across multiple instance families and configurations, providing a benchmark that identifies the optimal instance type and model server settings to minimize latency for a given model and payload. This data-driven approach directly addresses the high-latency issue without requiring code changes or switching to a different inference paradigm.

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Switch to asynchronous inference endpoint.

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Use SageMaker Elastic Inference to attach an accelerator.

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Compile the model using SageMaker Neo.

Question 4

A company uses Amazon SageMaker to host a model for fraud detection. The model uses a custom XGBoost container. The endpoint receives about 100 requests per second, each with 50 features. The team notices that the model's predictions are occasionally incorrect for a subset of requests. Which approach should the team take to debug the issue?

Accepted Answer

Enable SageMaker Model Monitor to capture and analyze inference data.. SageMaker Model Monitor captures inference data (input features and predictions) and compares them against a baseline to detect data drift or quality issues. This allows the team to identify if incorrect predictions stem from distribution shifts or anomalous input patterns, which is the most direct debugging approach for sporadic prediction errors.

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Use SageMaker Debugger to capture tensors during inference.

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Scale the endpoint to more instances to reduce load.

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Enable detailed CloudWatch Logs for the endpoint.

Question 5

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 6

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 exposes an objective metric parameter, and for binary classification with severe class imbalance, selecting F1, AUC, or Average Precision directs the AutoML search to optimize for minority-class performance rather than raw accuracy. Accuracy on a 1% positive dataset would be trivially high by predicting the majority class, so the objective must be a metric that rewards correct positive predictions. Setting the objective to F1 or AUC is the supported, documented way to steer Autopilot toward imbalance-aware models.

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 7

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.

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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 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

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.

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The policy is missing sagemaker:DescribeEndpoint.

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The policy is missing sagemaker:InvokeEndpoint.

Question 10

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.

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

Question 11

An ML team is deploying a model to a SageMaker endpoint for real-time inference. The model is large (2 GB) and requires GPU for low-latency inference. The team wants to minimize cost while maintaining a response time of under 200 ms. Which instance configuration and SageMaker feature would be best?

Accepted Answer

Use a GPU instance (ml.p3.2xlarge) with SageMaker Elastic Inference.. The best choice. By using a GPU instance (ml.p3.2xlarge) combined with SageMaker Elastic Inference, the team can attach a fractional GPU accelerator to the instance, providing the necessary GPU compute for low-latency inference while minimizing cost compared to using a full GPU instance alone. Option B (batch transform) is designed for offline inference on large datasets, not real-time. Option C (serverless inference) typically uses CPU instances and may incur cold starts, which can exceed the 200 ms response time requirement. Option D (multi-model endpoint) is optimized for hosting multiple models on a single instance, but does not directly address GPU acceleration or cost minimization for a single large model.

Answer

Use a batch transform job on GPU instances.

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Use a serverless inference endpoint with a CPU instance.

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Use a multi-model endpoint on a GPU instance.

Question 12

A company is deploying a machine learning model on Amazon SageMaker. The model needs to be updated frequently with new versions. The team wants to minimize downtime and test the new model version before routing all traffic to it. Which TWO strategies should be used together?

Accepted Answer

Use Amazon SageMaker canary deployment.. Option D (Amazon SageMaker canary deployment) is correct because a canary deployment lets you shift a small percentage of inference traffic to the new model version first, so you can validate its behavior in production while the old version continues serving the majority of requests, minimizing risk and downtime. Option E (Amazon SageMaker blue/green deployment) is correct because it provisions the new model version in a separate environment and only switches all traffic over once it is verified, providing a safe, near-zero-downtime cutover and a quick rollback path if issues arise. Together, canary and blue/green patterns satisfy the requirement to test a new version before routing all traffic while keeping downtime minimal. Option A (rolling update) is not a SageMaker model-hosting traffic-shifting strategy and does not provide pre-cutover validation of a new model version. Option B (multi-model endpoint) is designed to host many models behind one endpoint to save resources, not to test a new version before full traffic routing. Option C (A/B testing) compares variants for experimentation but does not by itself provide the controlled progressive rollout and safe full cutover that the scenario requires.

Answer

Use a rolling update strategy.

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Use a multi-model endpoint.

Answer

Use Amazon SageMaker A/B testing.

Question 13

A machine learning engineer is using Amazon SageMaker to train a deep learning model. The training job is failing with a 'ResourceLimitExceeded' error. The engineer checks the account limits and sees that the current limit for the instance type is 2, and they are already using 2 instances for other jobs. Which approach would resolve the issue MOST cost-effectively?

Accepted Answer

Use a different instance type that is available and has sufficient capacity. The ResourceLimitExceeded error occurs because the account has hit the service quota for the specific instance type (2 instances, both in use). Switching to a different instance type that has available capacity and sufficient resources for the training job resolves the failure immediately without waiting for a quota increase or disrupting other running jobs. This is the most cost-effective option because it avoids the delay and administrative overhead of a limit increase and does not require stopping existing workloads.

Answer

Request a service limit increase for the current instance type

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Use a managed spot training instead of on-demand

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Stop the other training jobs to free up resources

Question 14

An engineer runs the AWS CLI command in the exhibit to create a SageMaker endpoint configuration. The endpoint is created successfully, but when invoked, the inference response is slow. The engineer wants to test with a different instance type. Which action should the engineer take?

Accepted Answer

Update the endpoint configuration with the new instance type and then update the endpoint. To change the instance type for an existing SageMaker endpoint, you must first update the endpoint configuration with the new instance type, then update the endpoint to use the updated configuration. Option D describes this correct procedure. Option B is incorrect because the update-endpoint API does not directly accept an instance type parameter; it updates the endpoint to use a new endpoint configuration. Option A creates a new endpoint, which is less efficient. Option C deletes the endpoint unnecessarily.

Answer

Create a new endpoint configuration and use it to create a new endpoint

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Modify the existing endpoint directly using the update-endpoint API with a new instance type parameter

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Delete the endpoint and create a new one with the desired instance type

Question 15

A data scientist creates a model resource in SageMaker using the JSON configuration in the exhibit. When creating an endpoint, the deployment fails with an error 'ModelError: Cannot find inference code'. What is the MOST likely cause?

Accepted Answer

The inference container environment does not specify SAGEMAKER_PROGRAM. The error 'Cannot find inference code' occurs because SageMaker requires the `SAGEMAKER_PROGRAM` environment variable in the inference container to specify the entry-point script (e.g., `inference.py`) inside the `model.tar.gz`. Without this variable, SageMaker does not know which script to execute for inference, causing the deployment to fail. Option C correctly identifies this missing environment variable as the root cause.

Answer

The model.tar.gz file is missing the model weights

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The ECR image does not exist

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The training container does not have the SAGEMAKER_PROGRAM variable