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

Question 1

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.

Answer

Prune decision trees in the model

Answer

Increase the number of instances behind the endpoint

Answer

Switch to a GPU instance type

Question 2

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.

Answer

SageMaker Notebook instance

Answer

SageMaker Processing job

Answer

SageMaker Batch Transform

Question 3

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.

Answer

Amazon SageMaker Ground Truth

Answer

Amazon CloudWatch Logs

Answer

Amazon SageMaker Clarify

Question 4

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. Option A is correct because the number of vCPUs directly determines how many parallel data-processing threads or worker processes the training job can run, which affects throughput for CPU-bound workloads such as data loading, augmentation, and traditional ML algorithms. Option B is correct because the instance's RAM must accommodate the model parameters, gradients, optimizer states, and the training batch in memory; insufficient memory causes out-of-memory failures or forces smaller batch sizes. Option E is correct because GPU requirements (type, count, and GPU memory) are a primary driver for deep learning training, since frameworks like TensorFlow and PyTorch rely on CUDA-enabled GPUs such as the P3 or G4dn families for accelerated matrix operations. Option C is not relevant because endpoint latency concerns real-time inference hosting, not the training job itself. Option D is not a factor in instance-type selection because the AWS region determines availability and pricing, but the instance type is chosen based on compute, memory, and accelerator needs regardless of region.

Answer

The endpoint latency requirement

Answer

The AWS region where the instance is launched

Question 5

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

Accepted Answer

Amazon SageMaker Pipelines. Amazon SageMaker Pipelines (A) is a purpose-built CI/CD service for machine learning that lets you define, automate, and orchestrate end-to-end ML workflows—including data preprocessing, training, tuning, evaluation, and model registration—as a directed acyclic graph (DAG) of steps, so it directly orchestrates an ML pipeline. AWS Step Functions (C) is a general-purpose serverless workflow orchestration service that uses state machines (Amazon States Language) to coordinate AWS services, including SageMaker, Lambda, and Glue, making it a valid choice for orchestrating ML pipelines. The other options do not provide pipeline orchestration: SageMaker Ground Truth (B) is a data-labeling service for building labeled datasets, Amazon Redshift (D) is a cloud data warehouse for analytics, and AWS Glue (E) is a serverless ETL service for data cataloging and transformation—none of these orchestrate the sequence of ML pipeline steps.

Answer

Amazon SageMaker Ground Truth

Answer

Amazon Redshift

Answer

AWS Glue

Question 6

Refer to the exhibit. A SageMaker training job is launched with the CLI command shown. The job fails with an error 'S3 data distribution type not supported for File mode'. What is the most likely fix?

Accepted Answer

Change TrainingInputMode to Pipe. The error 'S3 data distribution type not supported for File mode' occurs because SageMaker's File mode downloads the entire dataset to the training instance's local storage before training begins, which does not support the ShardedByS3Key data distribution type. Changing TrainingInputMode to Pipe mode resolves this, as Pipe mode streams data directly from S3 to the algorithm, allowing each instance to receive a unique shard of the data without requiring local storage.

Answer

Change InstanceCount to 2

Answer

Increase VolumeSizeInGB to 100

Answer

Increase MaxRuntimeInSeconds to 7200

Question 7

A machine learning team is deploying a model using Amazon SageMaker. The model inference code runs on GPUs and requires a custom container. The team wants to minimize cold start latency. Which SageMaker hosting option should they use?

Accepted Answer

Use a real-time endpoint with a single production variant using a GPU instance.. A real-time endpoint with a single production variant using a GPU instance provides a persistent, always-on inference environment. This eliminates cold start latency entirely, as the container and GPU resources are pre-warmed and ready to serve requests immediately, which is critical for minimizing latency in GPU-based custom containers.

Answer

Use a multi-model endpoint with GPU instances.

Answer

Use a serverless inference endpoint.

Answer

Use a real-time endpoint with multiple production variants for redundancy.

Question 8

A data scientist is training a deep learning model on a large dataset using SageMaker. The training job is taking too long. Upon reviewing the CloudWatch logs, the scientist notices that the GPU utilization is below 10% most of the time. Which change is MOST likely to improve GPU utilization and reduce training time?

Accepted Answer

Increase the batch size in the training script.. Low GPU utilization (below 10%) indicates that the GPU is idle most of the time, waiting for data to be fed. Increasing the batch size allows each training step to process more samples per forward/backward pass, keeping the GPU busy with larger matrix operations and reducing the relative overhead of data loading and kernel launches. This directly improves GPU throughput and reduces total training time.

Answer

Use a different optimizer that requires less computation.

Answer

Switch to a smaller instance type to reduce data transfer overhead.

Answer

Reduce the size of the training dataset.

Question 9

A company is using Amazon SageMaker to train a model and wants to track hyperparameter tuning jobs. Which AWS service is BEST suited to store and query metadata such as tuning job configurations and results?

Accepted Answer

Amazon SageMaker Experiments. Amazon SageMaker Experiments is purpose-built for tracking, organizing, and querying metadata from machine learning training runs, including hyperparameter tuning jobs. It automatically captures configurations, metrics, and results, and provides a Python SDK and SDK API to search and compare trials, making it the best choice for this use case.

Answer

Amazon CloudWatch Logs

Answer

Amazon S3 with Amazon Athena

Answer

Amazon DynamoDB

Question 10

A company is using Amazon SageMaker to train a model and wants to automatically retrain the model every week using new data. Which AWS service should be used to orchestrate the retraining pipeline?

Accepted Answer

AWS Step Functions. AWS Step Functions is the correct choice because it provides a serverless workflow orchestration service that can coordinate multiple AWS services (e.g., SageMaker training jobs, Lambda functions, and data processing) into a state machine. It supports scheduling via Amazon EventBridge (formerly CloudWatch Events) to trigger the pipeline weekly, and it can handle retries, error handling, and parallel execution, making it ideal for automating a retraining pipeline.

Answer

Amazon CloudWatch Events

Answer

AWS Lambda

Answer

AWS Data Pipeline

Question 11

A machine learning team is using SageMaker to train a model. The training data is stored in an S3 bucket encrypted with AWS KMS. The training job fails with an 'AccessDenied' error. Which IAM permission is MOST likely missing from the SageMaker execution role?

Accepted Answer

kms:Decrypt. The training data is stored in an S3 bucket encrypted with AWS KMS. When SageMaker reads the encrypted data, the execution role must have permission to decrypt the KMS key. Without `kms:Decrypt`, the role cannot access the encrypted objects, resulting in an 'AccessDenied' error even if S3 read permissions are present.

Answer

s3:GetObject

Answer

s3:ListBucket

Answer

kms:GenerateDataKey

Question 12

A company is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection. The labeling job is taking longer than expected. The team notices that many workers are spending a lot of time on images with no objects. Which labeling strategy should they use to reduce costs and time?

Accepted Answer

Create a pre-labeling task where workers only identify if an object exists, then send only positive images for full labeling.. It introduces a two-stage labeling workflow: first, workers perform a quick binary classification to identify images containing objects, and only those positive images proceed to the expensive, time-consuming bounding box annotation. This directly reduces the cost and time spent on empty images, which is the root cause of the delay.

Answer

Use a private workforce instead of public.

Answer

Use automated data labeling with a pre-trained model to filter empty images.

Answer

Increase the number of workers per dataset object.

Question 13

Which TWO factors should be considered when choosing between Amazon SageMaker's real-time endpoints and serverless inference? (Select TWO.)

Accepted Answer

GPU requirement. Option A (GPU requirement) is correct because SageMaker serverless inference does not support GPU instances, so any model needing GPU acceleration must use real-time endpoints, which can be backed by GPU-enabled instance types such as ml.g4dn or ml.p3. Option B (Inference traffic pattern) is correct because serverless inference is designed for intermittent or unpredictable traffic and scales to zero when idle, whereas real-time endpoints suit steady, low-latency production traffic and remain provisioned. Option C is not a deciding factor since both endpoint types can be invoked from Lambda via the SageMaker Runtime InvokeEndpoint API. Option D is irrelevant because built-in algorithms are available regardless of the hosting option chosen. Option E is not a primary differentiator, as serverless inference supports models up to a documented size limit but model size alone does not dictate the choice between the two.

Answer

Integration with AWS Lambda

Answer

Availability of built-in algorithms

Answer

Model size in GB

Question 14

Which THREE measures can help reduce inference latency for a deep learning model deployed on SageMaker real-time endpoints? (Select THREE.)

Accepted Answer

Enable SageMaker Neo to compile the model.. Option A is correct because SageMaker Neo compiles the trained model into an optimized executable for the target hardware, applying operator fusion, quantization, and framework-specific optimizations that reduce per-inference compute and thus lower latency. Option C is correct because GPU instances provide massive parallel throughput for the matrix/tensor operations in deep learning, so inference completes faster than on comparable CPU instances for most models. Option D is correct because reducing input data size (e.g., lower-resolution images) shrinks the tensors that must be transferred and processed through every layer, directly cutting the compute and memory-bandwidth cost of each forward pass. Option B is not correct because increasing batch size improves throughput (requests per second) but typically increases the latency of an individual inference, since the endpoint must wait to assemble and process a larger batch. Option E is not correct because multi-model endpoints share one instance across multiple models to save hosting cost; they do not inherently reduce the latency of a single model's inference and can even add loading overhead.

Answer

Increase the batch size for inference.

Answer

Use a multi-model endpoint to share the instance.

Question 15

Which TWO actions are best practices for securing a SageMaker notebook instance? (Select TWO.)

Accepted Answer

Disable direct internet access for the notebook instance.. Disabling direct internet access for a SageMaker notebook instance (Option A) is a best practice because it prevents the instance from reaching the public internet, reducing the attack surface. This forces all outbound traffic through a VPC, allowing you to control egress via NAT gateways or VPC endpoints, and ensures data does not traverse the public internet. It is a fundamental security hardening step for sensitive workloads.

Answer

Enable root access for users to install packages.

Answer

Store data in the notebook's local storage for performance.

Answer

Use a shared IAM user for all data scientists.