MLA-C01 Practice Test 33 — 15 Questions

Question 1

A company is deploying a ML model for real-time fraud detection using SageMaker. The model must process requests within 50 ms and scale to handle up to 10,000 requests per second during peak hours. The data includes PII, so all traffic must stay within a VPC. The team has configured the SageMaker endpoint with a VPC and an internet gateway for model downloads. During a load test, the endpoint fails to achieve the required throughput. Which change would most likely resolve the issue?

Accepted Answer

Use VPC endpoints (interface endpoint for SageMaker and gateway endpoint for S3) to keep traffic within AWS backbone.. The correct answer is B because the endpoint is currently using an internet gateway for model downloads, which forces traffic out to the public internet and back, adding latency and risking throughput failures. By using VPC interface endpoints for SageMaker and gateway endpoints for S3, all traffic stays within the AWS backbone network, reducing network overhead and meeting the 50 ms latency requirement. This also keeps PII traffic within the VPC, satisfying security constraints.

Answer

Remove the VPC configuration and use public endpoints to reduce network overhead.

Answer

Add a NAT gateway to allow the SageMaker endpoint to access the internet efficiently.

Answer

Increase the instance count and use a larger instance type to handle the throughput.

Question 2

An e-commerce company uses Amazon SageMaker to deploy a real-time inference endpoint for product recommendations. The endpoint receives bursty traffic, with occasional spikes. The company wants to minimize cost while ensuring that latency remains under 100 ms. Which approach should the company take?

Accepted Answer

Deploy the model on a multi-model endpoint with automatic scaling and configure a warm-up period for new instances.. Option D is correct because a multi-model endpoint with automatic scaling allows multiple models to share a single endpoint, reducing cost while handling bursty traffic. Configuring a warm-up period ensures new instances are fully initialized before receiving traffic, preventing cold-start latency spikes and keeping inference under 100 ms.

Answer

Use an elastic inference accelerator to reduce latency instead of scaling.

Answer

Use a scheduled scaling plan based on historical traffic patterns.

Answer

Deploy the model on one large instance to handle peak load.

Question 3

A company wants to deploy a trained model to a SageMaker endpoint with automatic scaling based on traffic. Which TWO configurations are required? (Choose two.)

Accepted Answer

Set up an Application Auto Scaling policy. Option C is correct because Application Auto Scaling is the AWS service that automatically adjusts the number of instances for a SageMaker endpoint based on demand. You define a scaling policy (e.g., target tracking, step scaling) that tells Auto Scaling when to add or remove instances, which is essential for handling variable traffic without manual intervention.

Answer

Use a multi-model endpoint

Answer

Enable data capture

Answer

Configure a lifecycle configuration

Question 4

A company wants to automate its machine learning pipeline using AWS CodePipeline and Amazon SageMaker. The pipeline should train a model, evaluate it, and if the evaluation passes, register the model in the SageMaker Model Registry. Which service should the company use to orchestrate the training and evaluation steps?

Accepted Answer

Amazon SageMaker Pipelines. Amazon SageMaker Pipelines is the correct choice because it is a purpose-built, fully managed service for creating end-to-end machine learning workflows directly within the SageMaker ecosystem. It natively integrates with SageMaker training jobs, processing jobs for evaluation, and the Model Registry for conditional registration, allowing the entire pipeline—train, evaluate, and conditionally register—to be defined as a directed acyclic graph (DAG) of steps without needing to stitch together separate services.

Answer

AWS CodePipeline

Answer

AWS Glue Workflows

Answer

AWS Step Functions

Question 5

Refer to the exhibit. A SageMaker endpoint is logging this error when processing inference requests that require database access. What is the most likely cause?

Accepted Answer

The endpoint lacks a VPC configuration with proper security groups. The error indicates that the SageMaker endpoint cannot connect to the database when processing inference requests. This is most likely because the endpoint is not configured with a VPC that includes proper security groups and network ACLs to allow outbound traffic to the database. Without a VPC configuration, the endpoint uses the default SageMaker network, which lacks the necessary routing and security rules to reach resources in a private subnet.

Answer

Data capture is not enabled

Answer

The endpoint instance type is too small

Answer

The model is not compatible with the instance

Question 6

A company wants to deploy a PyTorch model on SageMaker for real-time inference. Which two steps are required? (Select TWO.)

Accepted Answer

Package the model artifacts into a tar.gz file.. Option C is correct because SageMaker requires model artifacts to be packaged as a single tar.gz file (containing the model weights, serialized PyTorch model, and any dependencies) for deployment. This compressed archive is uploaded to S3 and referenced when creating the model object for real-time inference.

Answer

Upload the training data to an S3 bucket.

Answer

Register the model in the SageMaker Model Registry.

Answer

Set up a SageMaker Notebook instance.

Question 7

A team is deploying a model using SageMaker Pipelines. They have defined a pipeline with steps: preprocessing, training, evaluation, and conditional registration. The evaluation step produces a JSON file with metrics. If accuracy > 0.9, the model is registered; else, the pipeline fails. Which TWO statements about this pipeline are correct? (Choose TWO.)

Accepted Answer

The evaluation step must output a JSON file in a specific format to be used by the condition step.. Option A is correct because the SageMaker Pipelines condition step expects the evaluation step to output a JSON file with a specific format, typically containing a metrics dictionary. The condition step then uses a property file to extract the accuracy value from that JSON, enabling the conditional logic to evaluate whether accuracy > 0.9.

Answer

The conditional step should be implemented as a separate Lambda function called from the pipeline.

Answer

The pipeline will automatically retry the training step if the condition fails.

Answer

The model registration step should be placed before the condition step to ensure the model is always registered.

Question 8

During deployment of a Hugging Face model, the endpoint logs show this error. Which step was likely missed?

Accepted Answer

The inference container does not include the transformers library; the team should use a pre-built Hugging Face container.. The error indicates that the inference container cannot find the `transformers` library, which is required to load and run the Hugging Face model. By using a pre-built Hugging Face container from AWS, the team ensures that all necessary dependencies (like `transformers`, `tokenizers`, and `torch`) are pre-installed and compatible with the SageMaker inference environment. Option A is correct because the most likely missed step was selecting a generic container instead of the purpose-built Hugging Face container.

Answer

The IAM role does not have permissions to download additional libraries.

Answer

The model artifact was not packaged correctly; the inference script is missing.

Answer

The endpoint configuration specifies the wrong instance type.

Question 9

Refer to the exhibit. An AWS IAM policy is attached to a role used by a CI/CD pipeline to deploy SageMaker endpoints. The pipeline attempts to create an endpoint configuration with a VPC subnet that is not subnet-0123456789abcdef0. What will happen when the pipeline tries to create the endpoint configuration?

Accepted Answer

The action will be denied because the Deny statement explicitly blocks CreateEndpointConfig when the subnet does not match.. Option A is correct because the IAM policy includes a Deny statement with a condition that explicitly blocks the `CreateEndpointConfig` action when the subnet specified in the request does not match `subnet-0123456789abcdef0`. Since the pipeline is attempting to create an endpoint configuration with a different subnet, the Deny statement overrides any Allow statements, resulting in the action being denied.

Answer

The action will be allowed because the CreateEndpoint statement allows all endpoints.

Answer

The action will be allowed only if the endpoint configuration uses a VPC with multiple subnets.

Answer

The action will be allowed because the policy lacks a Deny on the subnet condition for the endpoint resource.

Question 10

A machine learning engineer needs to deploy a TensorFlow model to Amazon SageMaker and wants to use the built-in TensorFlow Serving container. What should the engineer provide in the model archive?

Accepted Answer

A tar.gz file containing the TensorFlow SavedModel.. The built-in TensorFlow Serving container in Amazon SageMaker expects a TensorFlow SavedModel packaged in a tar.gz archive. This is because TensorFlow Serving natively loads models from the SavedModel format, which includes the model's computational graph, weights, and assets in a standardized directory structure. Providing a tar.gz of the SavedModel ensures compatibility with the container's default serving stack without requiring custom inference code.

Answer

A frozen graph of the TensorFlow model.

Answer

Model artifacts and a Python inference script.

Answer

A Dockerfile and model artifacts.

Question 11

A data science team has trained a model using SageMaker and wants to deploy it to a production endpoint with automatic scaling based on request volume. Which SageMaker feature should they use to configure scaling?

Accepted Answer

SageMaker Endpoint Autoscaling. SageMaker Endpoint Autoscaling is the correct feature because it automatically adjusts the number of instances behind a SageMaker hosted endpoint based on a target metric (e.g., requests per minute, CPU utilization) using Application Auto Scaling. This allows the endpoint to handle varying request volumes without manual intervention, ensuring cost efficiency and performance.

Answer

SageMaker Debugger

Answer

SageMaker Model Registry

Answer

SageMaker Pipelines

Question 12

A data scientist is using Amazon SageMaker Studio to develop a model. The training job is taking longer than expected. The data scientist suspects that the data is being downloaded from Amazon S3 each time the training starts. What is the BEST way to reduce data loading time?

Accepted Answer

Use SageMaker Pipe Input mode to stream data directly from S3.. SageMaker Pipe input mode streams data directly from S3 into the training algorithm without first downloading it to the training instance's local storage. This eliminates the bottleneck of copying entire datasets, reducing startup time and disk usage. It is the most direct and efficient way to address the issue of repeated downloads from S3.

Answer

Enable S3 transfer acceleration and cache the data in S3.

Answer

Use a larger instance type with more network bandwidth.

Answer

Use Amazon FSx for Lustre to mount a high-performance file system.

Question 13

A company wants to deploy its trained model to edge devices such as cameras and IoT devices. The model must run efficiently with low latency and minimal memory footprint. Which THREE actions should the company take to prepare the model for edge deployment? (Choose THREE.)

Accepted Answer

Use SageMaker Edge Manager to package and manage the model on devices.. SageMaker Edge Manager is purpose-built to package, optimize, and manage machine learning models on edge devices. It provides model packaging, runtime monitoring, and over-the-air updates, ensuring the model runs efficiently with low latency and minimal memory footprint on resource-constrained hardware like cameras and IoT devices.

Answer

Increase the model's complexity to improve accuracy on edge devices.

Answer

Deploy the model directly as a SageMaker endpoint and have the edge devices call it over the internet.

Question 14

A data science team deploys a PyTorch model on Amazon SageMaker for real-time inference. The model requires GPU for low latency. Which instance type is MOST cost-effective while meeting the GPU requirement?

Accepted Answer

ml.p3.2xlarge. Option C (ml.p3.2xlarge) is correct because it provides a GPU (NVIDIA V100) necessary for low-latency PyTorch inference on SageMaker, while being the most cost-effective among GPU options. The ml.p3.2xlarge offers a single GPU with sufficient compute for many real-time inference workloads, avoiding the higher cost of larger instances like ml.p4d.24xlarge.

Answer

ml.m5.2xlarge

Answer

ml.p4d.24xlarge

Answer

ml.c5.2xlarge

Question 15

A healthcare company is deploying a model for predicting patient outcomes. The model must be deployed across multiple AWS accounts to meet compliance requirements. Each account has its own Amazon SageMaker endpoint. The company wants to centralize monitoring of model performance without exposing data across accounts. Which solution should the company use?

Accepted Answer

Use SageMaker Model Monitor in each account and publish custom metrics to a central CloudWatch account using cross-account observability.. Option C is correct because it uses SageMaker Model Monitor in each account to detect data drift and model degradation locally, then publishes custom metrics to a central CloudWatch account via cross-account observability. This approach centralizes monitoring without moving raw inference data across accounts, satisfying the compliance requirement of not exposing data.

Answer

Establish VPC peering between accounts and call the endpoints from a central monitoring service.

Answer

Replicate the inference data to a central S3 bucket in the management account using cross-account replication, then run Model Monitor centrally.

Answer

Create a shared SageMaker Model Registry across accounts and aggregate monitoring.