MLS-C01 Machine Learning Implementation and Operations Practice Test 14 — 15 Questions

Question 1

A company is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires GPU inference. The company wants to minimize latency and cost. Which instance type and deployment strategy should be used?

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

Accepted Answer

Use a real-time endpoint with a GPU instance and enable multi-model endpoints.. Using a real-time endpoint with a GPU instance and enabling multi-model endpoints allows the company to serve multiple models on a single GPU instance, reducing cost by sharing the GPU resource while maintaining low latency for real-time inference. Multi-model endpoints load and unload models on demand, minimizing idle GPU time and optimizing cost without sacrificing the low-latency requirement.

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Use a serverless inference endpoint with a GPU instance.

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Use a batch transform job with a GPU instance.

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Use an asynchronous inference endpoint with a GPU instance.

Question 2

A data scientist is training a model using Amazon SageMaker with a custom Docker container. The training job fails with an error: 'Resource exhausted: Out of memory'. The training data is stored in S3. What should the data scientist do to resolve this issue?

Accepted Answer

Increase the instance memory by selecting a larger instance type.. The 'Resource exhausted: Out of memory' error indicates that the training instance's RAM is insufficient for the workload. Selecting a larger instance type with more memory directly addresses the OOM condition by providing additional physical RAM for model parameters, data batches, and intermediate computations. In SageMaker, instance types like ml.p3.2xlarge (61 GB RAM) vs. ml.p3.8xlarge (244 GB RAM) offer different memory capacities, and upgrading resolves memory exhaustion without altering the training logic.

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Increase the EBS volume size attached to the training instance.

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Use Pipe mode for data loading instead of File mode.

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Reduce the batch size in the training script.

Question 3

A machine learning engineer needs to deploy a model that performs real-time fraud detection. The model must be highly available and scalable. Which AWS service should be used to host the model?

Accepted Answer

Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints are designed for low-latency, synchronous inference, making them ideal for real-time fraud detection. They automatically scale across multiple instances and Availability Zones, providing high availability and elasticity to handle variable traffic loads without manual intervention.

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AWS Lambda

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Amazon ECS with a custom container

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Amazon SageMaker batch transform

Question 4

A company is using Amazon SageMaker to train a model. The training job is taking too long. The data scientist notices that the GPU utilization is low. Which action should be taken to improve training performance?

Accepted Answer

Increase the batch size in the training script.. Low GPU utilization during training typically indicates that the GPU is waiting for data, often due to a small batch size that underutilizes the GPU's parallel processing capacity. Increasing the batch size allows the GPU to process more samples per step, improving computational efficiency and throughput, which directly addresses the low utilization issue.

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Increase the number of training instances.

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Use spot instances to reduce cost.

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Decrease the batch size to reduce memory usage.

Question 5

A data scientist is using Amazon SageMaker Debugger to monitor training jobs. The training loss is decreasing but then suddenly spikes. What is the most likely cause and how should it be addressed?

Accepted Answer

Gradient explosion; apply gradient clipping.. A sudden spike in training loss after a period of decreasing loss is a classic symptom of gradient explosion, where gradients become excessively large during backpropagation, causing the model parameters to update erratically. Amazon SageMaker Debugger can monitor tensors and gradients in real time, and applying gradient clipping (e.g., via `max_grad_norm` in PyTorch or `clip_by_global_norm` in TensorFlow) directly addresses this by capping the gradient norm to prevent destabilizing updates.

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Overfitting; apply regularization.

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Learning rate too low; increase learning rate.

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Vanishing gradients; use ReLU activation.

Question 6

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 7

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 8

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 9

A data scientist needs to process a large dataset (100 TB) for training a machine learning model. The data is stored in Amazon S3. Which approach is most cost-effective and efficient for data processing?

Accepted Answer

Use Amazon EMR with Apache Spark.. Amazon EMR with Apache Spark is the most cost-effective and efficient approach for processing 100 TB of data stored in S3 because it provides a managed, scalable cluster that can process large datasets in parallel using in-memory computation. EMR integrates natively with S3 via the EMRFS connector, allowing data to be read directly from S3 without the need for intermediate storage, and it supports auto-scaling and spot instances to reduce costs. For petabyte-scale data, Spark's distributed processing engine outperforms single-node solutions and is more flexible than SQL-only or ETL-only services.

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Use AWS Glue ETL jobs.

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Use Amazon Athena to run SQL queries.

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Use Amazon SageMaker Processing with a single large instance.

Question 10

A company is deploying a machine learning model using Amazon SageMaker. The model must be updated frequently without downtime. Which TWO strategies can achieve this? (Choose two.)

Accepted Answer

Use blue/green deployment with endpoint variants.. Amazon SageMaker supports blue/green deployment using endpoint variants, where you can deploy a new model version alongside the current one and then shift all traffic to the new variant once validated. This approach ensures zero downtime because the existing endpoint remains active during the transition, and traffic is switched atomically. Option E is correct because canary deployment with SageMaker allows you to gradually shift a small percentage of traffic to a new model variant, monitor its performance, and then ramp up to 100% if successful, all without interrupting the service.

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Update the model artifact on the existing endpoint.

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Delete the existing endpoint and create a new one.

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Use rolling update with multiple instances.

Question 11

A data scientist is using Amazon SageMaker to train a model and wants to track experiments, including parameters and metrics. Which THREE actions should be taken? (Choose three.)

Accepted Answer

Create an experiment in SageMaker Experiments.. SageMaker Experiments is the native service for organizing, tracking, and comparing machine learning training runs. It provides a structured way to create experiments, trials, and trial components, enabling systematic tracking of parameters and metrics without manual overhead.

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Use SageMaker Studio to manually record experiments.

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Use Amazon CloudWatch Logs to store experiment data.

Question 12

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).. Output data configuration (e.g., model artifact path) is required because SageMaker needs to know where to save the trained model artifacts (e.g., the model.tar.gz file) in Amazon S3. Without this path, the training job cannot complete successfully as it has no destination for the output.

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VPC configuration for network isolation.

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Hyperparameters for the algorithm.

Question 13

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 14

A data scientist is deploying a machine learning model using SageMaker and wants to automate the retraining pipeline. The training data is updated daily in an S3 bucket. Which combination of AWS services should the data scientist use to trigger a new training job when new data arrives?

Accepted Answer

Amazon CloudWatch Events to invoke an AWS Lambda function that starts a SageMaker training job. Amazon S3 event notifications can be sent to Amazon CloudWatch Events (via Amazon EventBridge), which then triggers an AWS Lambda function. The Lambda function contains code to start a SageMaker training job using the boto3 SDK. This serverless architecture provides a fully automated, event-driven pipeline that responds immediately when new data arrives in the S3 bucket, without polling or manual intervention.

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Amazon SQS queue to store S3 events and a cron job to poll and start training

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Use SageMaker Pipelines with a schedule to check for new data every hour

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Amazon S3 event notification to directly start a SageMaker training job

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Amazon CloudWatch Events to run an AWS Step Functions state machine that starts a SageMaker training job

Question 15

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