MLS-C01 Machine Learning Implementation and Operations Practice Test 5 — 15 Questions

Question 1

A team is using SageMaker to train a model using the built-in XGBoost algorithm. The training job is taking longer than expected. The team suspects that the data is not being loaded efficiently. Which data format should they use to minimize training time?

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

Accepted Answer

Pipe mode with RecordIO-Protobuf. Pipe mode with RecordIO-Protobuf is correct because it streams data directly from Amazon S3 to the training container without writing to disk, reducing I/O overhead. RecordIO-Protobuf also stores data in a binary, columnar format that XGBoost can parse more efficiently than CSV, especially for large datasets, leading to faster training times.

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Pipe mode with CSV

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File mode with Parquet

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File mode with CSV

Question 2

A company uses SageMaker Ground Truth to label images for object detection. After labeling, they notice that the bounding boxes are often misaligned with the objects. Which action should they take to improve label quality?

Accepted Answer

Use a pre-built annotation tool that enforces bounding box alignment. SageMaker Ground Truth offers pre-built annotation tools, such as the bounding box tool, which includes features like snap-to-grid or edge alignment that enforce precise bounding box placement. Using this tool directly improves label quality by reducing human error in manual drawing, ensuring boxes tightly fit objects without manual guesswork.

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Use automated labeling with a pre-trained model

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Increase the number of workers per task

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Adjust the confidence threshold for the model

Question 3

A data scientist needs to create a SageMaker notebook instance with access to a private S3 bucket. The bucket uses SSE-KMS encryption. Which additional configuration is required?

Accepted Answer

Attach a policy to the notebook's IAM role that allows kms:Decrypt. When an S3 bucket uses SSE-KMS encryption, the SageMaker notebook instance's IAM role must include a policy that allows the kms:Decrypt action. This is necessary because SageMaker needs to decrypt the data using the KMS key when reading objects from the bucket. Without this permission, the notebook instance will fail to access the encrypted S3 objects, even if the bucket policy allows s3:GetObject.

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Add a lifecycle configuration script

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Modify the bucket policy to allow s3:GetObject

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Place the notebook instance in a VPC

Question 4

You are building a CI/CD pipeline for SageMaker using AWS CodePipeline. Which THREE components are essential for a fully automated model training and deployment pipeline?

Accepted Answer

AWS CodeCommit to store the training script and model code. AWS CodeCommit is essential because it provides a secure, scalable Git repository to store the training script and model code, which serves as the source stage in the CI/CD pipeline. This ensures version control and triggers the pipeline automatically on code changes, enabling a fully automated workflow.

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AWS CodeDeploy to deploy the model to an endpoint

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AWS CloudFormation to define the infrastructure

Question 5

You are deploying a PyTorch model to a SageMaker endpoint. The model is large (5 GB) and the endpoint is using an ml.c5.2xlarge instance. Inference latency is higher than required. Which change would most effectively reduce latency?

Accepted Answer

Use a GPU instance type such as ml.p3.2xlarge. The primary bottleneck for a large PyTorch model (5 GB) on a CPU instance (ml.c5.2xlarge) is the lack of GPU acceleration for matrix operations and tensor computations. Switching to a GPU instance like ml.p3.2xlarge (with NVIDIA V100 GPUs) offloads the heavy parallel computation to the GPU, drastically reducing per-inference latency for deep learning models, especially those with large parameter counts.

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Reduce the batch size in the inference code

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Decrease the number of model server workers

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Enable SageMaker Elastic Inference

Question 6

A data scientist needs to deploy a PyTorch model for real-time inference. Which AWS service is best suited for this task?

Accepted Answer

Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints are purpose-built for hosting ML models that require low-latency, synchronous inference. They automatically manage the underlying infrastructure, including scaling, load balancing, and health checks, and support custom PyTorch containers via the SageMaker inference toolkit. This makes them the optimal choice for deploying a PyTorch model for real-time inference.

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Amazon SageMaker Batch Transform

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Amazon ECS with Fargate

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AWS Lambda with custom container

Question 7

A company uses SageMaker to train a model, but the training job fails due to insufficient memory. What is the most cost-effective way to resolve this?

Accepted Answer

Use a larger instance type with more memory. Increasing instance memory directly addresses the memory issue. Option B is wrong because Spot Instances do not provide additional memory; they are a pricing model. Option C is wrong because reducing batch size may not solve memory issues if the model itself is large, and it can affect training dynamics. Option D is wrong because distributed training adds complexity and cost, and may be overkill.

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Use Spot Instances to reduce cost

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Reduce the batch size in the training script

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Switch to distributed training across multiple instances

Question 8

A team wants to automate the retraining of a model weekly using new data that arrives in S3. Which combination of services should they use?

Accepted Answer

Amazon SageMaker Pipelines and S3 events. Amazon SageMaker Pipelines natively supports automated retraining workflows triggered by S3 events. When new data arrives in S3, an event notification can invoke a Lambda function that starts a pipeline execution, which includes steps for data processing, training, evaluation, and model registration. This provides a fully managed, repeatable, and auditable MLOps pipeline without custom orchestration code.

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AWS Lambda and S3 events

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Amazon SageMaker Processing jobs

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AWS Step Functions and AWS Glue

Question 9

A deployed SageMaker endpoint is returning high latency. The model is a scikit-learn Random Forest. Which action is most likely to reduce latency?

Accepted Answer

Reduce the number of trees in the ensemble. Reducing the number of trees in a Random Forest ensemble directly decreases the total number of decision paths that must be evaluated per inference request. Since each tree contributes additively to the prediction time, fewer trees means fewer sequential or parallel evaluations, which lowers the per-request latency at the cost of some model accuracy.

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Prune decision trees in the model

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Increase the number of instances behind the endpoint

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Switch to a GPU instance type

Question 10

A data scientist needs to run a hyperparameter tuning job for a deep learning model. Which SageMaker feature should they use?

Accepted Answer

SageMaker Hyperparameter Tuning Job. SageMaker Hyperparameter Tuning Job (option A) is the correct feature because it is the native SageMaker capability designed specifically to automate the search for optimal hyperparameters for a machine learning model. It launches multiple training jobs with different hyperparameter combinations, evaluates them against a specified objective metric, and uses strategies like Bayesian optimization or random search to converge on the best configuration. This directly matches the requirement to run a hyperparameter tuning job for a deep learning model.

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SageMaker Experiments

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SageMaker Automatic Model Tuning

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SageMaker Processing

Question 11

A company wants to serve predictions from a model using a REST API with low latency. Which SageMaker deployment option is most appropriate?

Accepted Answer

SageMaker real-time endpoint. SageMaker real-time endpoints are designed for low-latency inference, deploying the model behind an HTTPS endpoint that autoscales to handle request traffic. This directly meets the requirement for serving predictions via a REST API with minimal latency, as the endpoint keeps the model loaded and ready to respond to individual requests in milliseconds.

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SageMaker Notebook instance

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SageMaker Processing job

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SageMaker Batch Transform

Question 12

A team notices that a SageMaker training job using TensorFlow is running slower than expected. The training data is in S3 in TFRecord format. Which action is most likely to improve training throughput?

Accepted Answer

Use Pipe mode for data ingestion. Pipe mode streams data directly from S3 into the training container without writing to disk, eliminating the I/O bottleneck of downloading TFRecord files first. Since TFRecords are already serialized for efficient reading, Pipe mode leverages this by feeding data sequentially, which reduces latency and improves throughput for TensorFlow jobs.

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Use distributed training with more instances

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Increase the batch size in the training script

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Switch from Pipe mode to File mode

Question 13

A company wants to monitor a deployed model for data drift. Which AWS service should they use?

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is the correct service because it is specifically designed to continuously monitor deployed machine learning models for data drift and quality issues. It automatically detects deviations in the input data distribution compared to a baseline, triggering alerts when drift exceeds defined thresholds, which directly addresses the company's requirement.

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Amazon SageMaker Ground Truth

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Amazon CloudWatch Logs

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Amazon SageMaker Clarify

Question 14

Which THREE factors should be considered when choosing an instance type for a SageMaker training job?

Accepted Answer

The number of vCPUs needed for parallel processing. The number of vCPUs directly determines the parallel processing capability of the training job. SageMaker training instances with more vCPUs can handle larger batch sizes and more concurrent data loading, which is critical for CPU-bound preprocessing or model training that does not rely on GPUs. Choosing an instance with insufficient vCPUs can lead to underutilization of other resources or excessive training time.

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The endpoint latency requirement

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The AWS region where the instance is launched

Question 15

Which TWO services can be used to orchestrate a machine learning pipeline?

Accepted Answer

Amazon SageMaker Pipelines. Amazon SageMaker Pipelines is a purpose-built service for creating, automating, and managing end-to-end machine learning workflows. It provides direct integration with SageMaker's training, tuning, and deployment steps, allowing you to define a directed acyclic graph (DAG) of ML steps that can be triggered on a schedule or by events. AWS Step Functions is a serverless orchestration service that lets you coordinate multiple AWS services into flexible workflows. It can orchestrate ML pipelines by integrating with SageMaker, Lambda, and other services, making it a viable alternative for complex, multi-step workflows that may span beyond SageMaker's native capabilities. Both services are capable of orchestrating ML pipelines.

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Amazon SageMaker Ground Truth

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Amazon Redshift

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AWS Glue