MLS-C01 Machine Learning Implementation and Operations Practice Test 20 — 15 Questions

Question 1

A company wants to perform automated hyperparameter tuning for a model. Which Amazon SageMaker feature should be used?

Accepted Answer

Amazon SageMaker automatic model tuning. Amazon SageMaker automatic model tuning (also known as hyperparameter tuning) is the correct feature because it automates the process of searching for the optimal combination of hyperparameters for a machine learning model. It uses algorithms like Bayesian optimization, random search, or Hyperband to efficiently explore the hyperparameter space and find the best-performing configuration based on a specified objective metric.

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Amazon SageMaker Clarify

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Amazon SageMaker Ground Truth

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Amazon SageMaker Debugger

Question 2

A machine learning engineer is building a pipeline using Amazon SageMaker Pipelines. The pipeline has multiple steps including data preprocessing, training, and evaluation. Which statement about SageMaker Pipelines is correct?

Accepted Answer

Pipelines support caching of step outputs.. SageMaker Pipelines supports output caching, which allows step outputs to be reused when the step configuration and inputs remain unchanged. This caching mechanism reduces execution time and cost by skipping redundant computations for steps like data preprocessing or training when their parameters have not changed.

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Steps in a pipeline must run sequentially.

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Pipelines can only use built-in algorithms.

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Pipelines cannot have conditional branches.

Question 3

A company is using Amazon SageMaker to deploy a model for real-time inference. The endpoint receives variable traffic and the company wants to optimize cost while maintaining responsiveness. Which scaling policy should be used?

Accepted Answer

Target tracking scaling based on invocation count. Target tracking scaling based on invocation count is the correct choice because it automatically adjusts the number of instances in real-time based on a predefined metric (e.g., InvocationsPerInstance), maintaining responsiveness during variable traffic while optimizing cost by scaling down during low demand. This policy uses a target value (e.g., 1000 invocations per instance) and SageMaker Application Auto Scaling continuously monitors CloudWatch metrics to add or remove instances as needed, eliminating manual intervention.

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Simple scaling with a cooldown period

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Scheduled scaling

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Manual scaling

Question 4

A machine learning engineer is setting up a training job in Amazon SageMaker. Which THREE components are required to define a training job? (Choose three.)

Accepted Answer

Output data configuration (e.g., model artifact path).. The three required components are C, D, and E. Option C (Output data configuration, e.g., model artifact path) is required because SageMaker needs an S3 location to write the resulting model artifacts after training completes. Option D (An algorithm or custom container image) is required because the training job must know which algorithm or Docker image to run to execute the training code. Option E (Input data configuration, e.g., S3 path) is required because SageMaker must know where the training dataset resides (typically in S3) to feed it to the algorithm. Options A and B are not required: VPC configuration is optional and only used for network isolation, and hyperparameters are optional since many algorithms have defaults and some require none.

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VPC configuration for network isolation.

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Hyperparameters for the algorithm.

Question 5

A company is training a deep learning model on Amazon SageMaker. The training job is failing with an out-of-memory error. Which SageMaker feature should the company use to resolve this issue without changing the instance type?

Accepted Answer

Use SageMaker distributed training with model parallelism. SageMaker's distributed training with model parallelism splits the model's layers across multiple GPUs, reducing the memory footprint per GPU. This allows the company to train a large model that exceeds a single GPU's memory without changing the instance type. Model parallelism is specifically designed to handle out-of-memory errors by distributing the model parameters, gradients, and optimizer states across devices.

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Use SageMaker Savings Plans

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Enable SageMaker Managed Spot Training

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Use SageMaker Debugger to monitor memory usage

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Enable SageMaker Profiler to profile memory

Question 6

A company is using SageMaker to train a large NLP model. The training job is taking too long due to high I/O wait time. The data is stored as CSV files in S3. Which optimization should the company implement to reduce I/O wait time?

Accepted Answer

Use SageMaker Pipe mode to stream data directly from S3. SageMaker Pipe mode streams data directly from S3 into the training algorithm without first writing it to the local disk, eliminating the I/O wait time caused by downloading and decompressing CSV files. This is the most effective optimization for high I/O wait during training because it bypasses the bottleneck of writing large datasets to the instance's local storage.

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Convert CSV files to RecordIO format

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Use SageMaker batch transform before training

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Use SageMaker File mode with larger instance storage

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Use SageMaker ShardedByS3Key data distribution

Question 7

A machine learning team is using SageMaker to build a model. They need to track hyperparameter tuning experiments, compare results, and visualize metrics. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is the feature designed to track, organize, and compare machine learning experiments, including hyperparameter tuning jobs. It automatically captures inputs, parameters, metrics, and artifacts for each trial, and provides a UI to visualize and compare results across runs. This directly matches the team's need to track tuning experiments and visualize metrics.

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SageMaker Ground Truth

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SageMaker Model Monitor

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SageMaker Hyperparameter Tuning

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SageMaker Debugger

Question 8

A company has deployed a model on SageMaker for real-time inference. The endpoint is experiencing high latency during traffic spikes. Which action should the company take to reduce latency?

Accepted Answer

Enable SageMaker endpoint auto-scaling. Enabling SageMaker endpoint auto-scaling allows the endpoint to dynamically adjust the number of instances based on incoming traffic, which directly reduces latency during spikes by ensuring sufficient compute capacity is available. Auto-scaling uses CloudWatch metrics (e.g., InvocationsPerInstance or latency) to trigger scale-out events, preventing queue buildup and response time degradation.

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Use a larger instance type for the endpoint

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Attach SageMaker Elastic Inference to the endpoint

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Use SageMaker Neo to compile the model

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Switch to SageMaker batch transform

Question 9

A data scientist is using SageMaker to train a model with a custom algorithm. The training script uses TensorFlow and runs on GPU instances. The training job fails with 'CUDA_ERROR_OUT_OF_MEMORY'. What is the most likely cause?

Accepted Answer

The batch size is too large for the GPU memory. The error 'CUDA_ERROR_OUT_OF_MEMORY' indicates that the GPU memory has been exhausted during training. In TensorFlow, the batch size directly determines how many samples are processed simultaneously on the GPU; a batch size that is too large will exceed the available GPU memory, causing this specific CUDA error. Reducing the batch size is the standard fix for this issue.

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The S3 bucket is in a different region

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The GPU driver is outdated

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The training script has a memory leak on CPU

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The instance type does not have enough CPU cores

Question 10

A company wants to use SageMaker to host multiple models behind a single endpoint to reduce costs. Which SageMaker feature should they use?

Accepted Answer

SageMaker Multi-Model Endpoints. SageMaker Multi-Model Endpoints allow you to deploy multiple models behind a single endpoint, each loaded dynamically from Amazon S3 based on the inference request. This reduces hosting costs by sharing a single instance across many models, as only the models that are actively invoked consume memory. The correct answer is E because this feature is specifically designed for cost-efficient multi-model hosting.

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SageMaker Elastic Inference

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SageMaker inference pipeline

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SageMaker batch transform

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SageMaker multi-container endpoints

Question 11

A machine learning team is using SageMaker to train a model. They want to ensure that the training data is encrypted at rest in the S3 bucket and that the data is also encrypted during transit. Which configuration should they use?

Accepted Answer

Use SSE-S3 encryption on the S3 bucket and enforce HTTPS. SSE-S3 provides server-side encryption at rest for objects in S3, and enforcing HTTPS ensures data is encrypted in transit using TLS. This combination meets the requirements for both encryption at rest and in transit without additional client-side complexity.

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Use client-side encryption and transfer data via HTTP

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Use SSE-KMS encryption on the S3 bucket and disable HTTP

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Use SSE-C encryption on the S3 bucket and HTTPS

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Use no encryption on S3 but use HTTPS

Question 12

A company is using SageMaker to train a model with a large dataset that is stored in S3. The training job is taking a long time due to high I/O latency. The team has already converted the data to RecordIO format. What should they do next to reduce I/O latency?

Accepted Answer

Use Pipe mode to stream the RecordIO data. Pipe mode streams data directly from S3 to the training algorithm in a sequential manner, eliminating the need to download files to the local disk. Since the data is already in RecordIO format, Pipe mode can efficiently read the serialized records, significantly reducing I/O latency compared to File mode.

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Use SageMaker fast file mode

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Use multiple training instances

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Use Amazon FSx for Lustre as the training data source

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Shuffle the data before training

Question 13

Which TWO of the following are benefits of using SageMaker Managed Spot Training? (Select TWO.)

Accepted Answer

Potential for significant cost savings. Option B is correct because SageMaker Managed Spot Training uses spare EC2 capacity that AWS prices at a discount of up to 90% versus On-Demand, so workloads can achieve significant cost savings. Option E is correct because the same underlying mechanism means the effective training cost is lower than running equivalent On-Demand instances, which is the primary reason to adopt Managed Spot Training. Note that these two benefits are closely related: the discount on spare capacity is what produces the lower cost relative to On-Demand. Option A is not correct because Spot capacity can be reclaimed with a two-minute interruption notice, so checkpointing to Amazon S3 is strongly recommended (and often required) to resume training. Option C is not correct because Spot instances are not inherently faster; training time depends on instance type and workload, and interruptions can actually lengthen wall-clock time. Option D is not correct because Spot capacity is not guaranteed—AWS can reclaim it when capacity is needed, which is precisely why Managed Spot Training must handle interruptions.

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No need to checkpoint the model

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Faster training times

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Guaranteed instance availability

Question 14

Which THREE of the following are valid ways to deploy a model using SageMaker? (Select THREE.)

Accepted Answer

Deploy to a SageMaker batch transform job. Option B is correct because a SageMaker batch transform job is a supported deployment pattern for running inference on large datasets stored in Amazon S3 without maintaining a persistent endpoint. Option C is correct because SageMaker asynchronous endpoints are a valid deployment option designed for large payloads and long-running inference requests with near-real-time latency. Option D is correct because SageMaker real-time endpoints are the standard deployment method for low-latency, synchronous inference with a persistent HTTPS endpoint. Option A is not a SageMaker deployment method; AWS Lambda can invoke a SageMaker endpoint but does not host the model itself. Option E is not a SageMaker deployment method either, since deploying directly to EC2 bypasses SageMaker hosting and is not one of its managed deployment options.

Answer

Deploy to AWS Lambda

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Deploy to Amazon EC2 directly

Question 15

An IAM policy attached to a SageMaker notebook role is shown in the exhibit. A data scientist is trying to run a training job from the notebook, but the job fails with an access denied error. The training job needs to read data from 'my-bucket' and write output to 'my-bucket'. What is the most likely cause of the failure?

Accepted Answer

The training job execution role does not have the same permissions. The IAM policy shown is attached to the SageMaker notebook role, which is used by the data scientist to interact with the notebook. However, when a training job is launched, it runs under a separate execution role (the SageMaker execution role for training jobs), not the notebook role. The training job fails because that execution role lacks the necessary S3 permissions (e.g., s3:GetObject, s3:PutObject) to read from and write to 'my-bucket'. The notebook role's permissions are irrelevant to the training job's runtime actions.

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The policy does not allow s3:ListBucket

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The policy does not allow sagemaker:CreateTrainingJob

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The S3 bucket is not specified in the Resource

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The policy does not allow s3:GetObject