MLA-C01 Deployment and Orchestration of ML Workflows Practice Test 4 — 15 Questions

Question 1

Refer to the exhibit. A user is unable to invoke a SageMaker endpoint. The IAM policy shown is attached to the user. Which permission is missing to allow invocation?

Accepted Answer

sagemaker:InvokeEndpoint. To invoke a SageMaker endpoint, the user needs the `sagemaker:InvokeEndpoint` permission. The IAM policy shown lacks this action, which is required for making real-time inference requests to the endpoint. Without it, any attempt to call the endpoint via the SDK or CLI will fail with an access denied error.

Answer

sagemaker:DescribeEndpoint

Answer

sagemaker:CreateEndpoint

Answer

sagemaker:ListEndpoints

Question 2

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 3

Refer to the exhibit. A SageMaker Pipeline fails with 'Invalid output reference' at the TrainingStep. What is the most likely cause?

Accepted Answer

The TrainingStep expects a single artifact but TuningStep produces multiple. Option C is correct because in SageMaker Pipelines, a `TrainingStep` that expects a single artifact as input will fail with 'Invalid output reference' if the preceding `TuningStep` produces multiple artifacts (e.g., from multiple training jobs). The pipeline cannot resolve which specific artifact to pass, causing the error.

Answer

The TuningStep output name is misspelled

Answer

The pipeline role lacks permissions

Answer

The instance type is incompatible

Question 4

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 5

A company uses Amazon SageMaker Pipelines to automate its ML workflow. The pipeline includes a training step and a model evaluation step. If the evaluation step fails, the pipeline should stop and notify the team. How should the company configure the pipeline?

Accepted Answer

Define a ConditionStep that checks the evaluation metric and fail the pipeline if the metric is below a threshold.. Option A is correct because SageMaker Pipelines natively supports a ConditionStep that can evaluate a metric (e.g., model accuracy) and branch the pipeline execution. By configuring the ConditionStep to check if the evaluation metric falls below a threshold, you can explicitly fail the pipeline and trigger a notification (e.g., via SNS) when the condition is not met. This is the idiomatic, pipeline-native way to halt execution on evaluation failure without external dependencies.

Answer

Use Amazon SageMaker Model Monitor to detect failures in the evaluation step.

Answer

Create an AWS Step Function state machine that monitors the pipeline and stops it on failure.

Answer

Configure an Amazon CloudWatch alarm on the evaluation step's execution time to stop the pipeline.

Question 6

A financial services company deploys multiple models on a single Amazon SageMaker endpoint using a multi-model endpoint (MME). The models are stored in Amazon S3. Each model is approximately 500 MB and is loaded on demand. Users report high latency for cold-start scenarios. What should the company do to reduce cold-start latency?

Accepted Answer

Configure the endpoint to use a larger 'ModelCacheSize' parameter.. Option D is correct because increasing the 'ModelCacheSize' parameter allows the SageMaker multi-model endpoint to keep more models loaded in memory, reducing the frequency of cold starts where a model must be downloaded from S3 and loaded into memory. This directly addresses the latency issue by caching models that are frequently accessed, avoiding repeated loading overhead.

Answer

Reduce the instance size to increase the number of instances per unit cost.

Answer

Increase the number of instances in the endpoint's auto-scaling group.

Answer

Deploy each model on a separate endpoint to avoid concurrent loading.

Question 7

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 8

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 9

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 10

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.

Question 11

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 12

A company deploys a model on Amazon SageMaker for real-time inference. The inference latency is too high. The model is a large deep learning model. The company wants to reduce latency without significantly impacting accuracy. Which approach should the company consider?

Accepted Answer

Use SageMaker Inference Recommender to test different instance types and optimizations.. SageMaker Inference Recommender is designed specifically to automate load testing and benchmarking across various instance types and model optimizations (e.g., Elastic Inference, GPU acceleration, serialization formats). It provides latency and throughput metrics to identify the optimal configuration for reducing inference latency while maintaining accuracy, making it the correct choice for a large deep learning model with high latency.

Answer

Increase the batch size for inference.

Answer

Use a smaller instance type to reduce inference time.

Answer

Enable SageMaker Model Monitor to detect performance issues.

Question 13

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 14

Refer to the exhibit. A data scientist creates a SageMaker Pipeline definition using the JSON shown. The pipeline runs successfully, but the scientist notices that the training step did not use the parameter 'TrainingInstanceCount' defined in Parameters. Why did this happen?

Accepted Answer

The steps do not reference the Parameters; the values are hardcoded in the step definitions.. Option C is correct because the SageMaker Pipeline definition shows that the training step's `InstanceCount` field is hardcoded to `1` in the step definition, rather than referencing the `TrainingInstanceCount` parameter using the `Parameters` object (e.g., `Parameters.TrainingInstanceCount`). In SageMaker Pipelines, parameters defined in the `Parameters` section must be explicitly referenced within the step definitions using the `Parameters` object; otherwise, the pipeline uses the hardcoded values and ignores the parameters entirely.

Answer

The pipeline encountered a runtime error and fell back to default values.

Answer

The parameter name has a typo; it should be 'TrainingInstanceCount' not 'TrainingInstanceCount'.

Answer

The training image is not compatible with the specified instance type.

Question 15

Refer to the exhibit. A company configures a SageMaker Model Monitor Data Quality monitoring schedule as shown. The schedule runs every hour. However, the team notices that the monitoring job fails intermittently with an AccessDenied error when accessing the S3 bucket for output. The IAM role SageMakerMonitorRole has permissions to write to s3://my-bucket/monitor-output. What is the MOST likely cause of the failure?

Accepted Answer

The monitoring job runs in a VPC that does not have an S3 VPC endpoint, and the bucket policy denies requests from outside the VPC.. The intermittent AccessDenied error when SageMaker Model Monitor attempts to write to the S3 output bucket strongly indicates a network or policy restriction. If the monitoring job is configured to run inside a VPC (common for security compliance) and that VPC lacks an S3 VPC endpoint, traffic to S3 traverses the public internet. If the S3 bucket policy explicitly denies requests from outside the VPC (using a condition like `aws:SourceVpce` or `aws:SourceVpc`), then jobs running inside the VPC without an endpoint will be denied access intermittently, especially if the job's execution role is assumed from within the VPC.

Answer

The S3UploadMode is set to Continuous, which is only supported for batch transform jobs.

Answer

The cron expression is invalid; it should use rate(1 hour) instead.

Answer

The baseline constraints and statistics files are missing from the S3 bucket.

MLA-C01 Deployment and Orchestration of ML Workflows Practice Test 4 — 15 Questions

Exhibit

MLA-C01 Deployment and Orchestration of ML Workflows Practice Test 4 — 15 Questions

Exhibit