MLA-C01 Deployment and Orchestration of ML Workflows Practice Test 7 — 15 Questions

Question 1

A company wants to deploy a model using a serverless inference endpoint that can automatically scale to zero when not in use and has a configurable maximum concurrency. Which SageMaker inference option meets these requirements?

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

Accepted Answer

Serverless inference. SageMaker Serverless Inference is the correct choice because it automatically scales to zero when the endpoint is idle, eliminating costs during periods of no traffic, and it allows you to configure a maximum concurrency limit per endpoint to control throughput. This fully managed, pay-per-invoke option is designed for workloads with intermittent or unpredictable traffic patterns, meeting both requirements precisely.

Answer

Real-time endpoint with auto-scaling

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Batch transform

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Asynchronous inference

Question 2

A machine learning engineer is deploying a TensorFlow model for real-time inference. The model has high latency on CPU. Which TWO actions can reduce inference latency? (Choose two.)

Accepted Answer

Attach Amazon Elastic Inference to the endpoint. Compiling with SageMaker Neo optimizes the model for the target hardware. Attaching Elastic Inference provides GPU acceleration without moving to a full GPU instance.

Answer

Enable SageMaker Model Monitor

Answer

Switch to a multi-model endpoint

Answer

Use a larger instance type with more vCPUs

Question 3

A team wants to deploy a new model using a canary deployment strategy on SageMaker. Which TWO configurations are necessary? (Choose two.)

Accepted Answer

Set the initial traffic distribution (e.g., 90% old, 10% new). A canary deployment requires two production variants (old and new) with traffic distribution. Gradual traffic shifting can be achieved by updating the endpoint's variant weights over time.

Answer

Create a CloudWatch alarm to automatically rollback

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Enable data capture on the endpoint

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Use SageMaker Model Registry to approve the new model

Question 4

A company uses SageMaker Pipelines to automate their ML workflow. They want to ensure that pipeline steps are not re-executed if the inputs and parameters have not changed since the last successful run. Which THREE features can help achieve this? (Choose three.)

Accepted Answer

Use a ConditionStep to skip steps if data is unchanged. Pipeline caching reuses step outputs when inputs/parameters are identical. SageMaker Experiments lineage can track previous run metadata. Using a ConditionStep with checksums can skip steps based on data content.

Answer

Deploy a real-time endpoint for data validation

Answer

Use SageMaker Model Monitor

Question 5

A data science team needs to deploy a trained PyTorch model for real-time inference with sub-100ms latency. The model fits on a single GPU. Which SageMaker inference option is MOST cost-effective while meeting the latency requirement?

Accepted Answer

SageMaker real-time endpoint on ml.g4dn.xlarge. SageMaker real-time endpoints provide dedicated, persistent instances that can handle synchronous inference with sub-100ms latency. The ml.g4dn.xlarge instance includes a single NVIDIA T4 GPU, which is sufficient for the model size and offers the lowest cost among GPU instances that meet the latency requirement. This option balances performance and cost for real-time, low-latency inference.

Answer

SageMaker Batch Transform

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SageMaker Async Inference

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SageMaker Serverless Inference

Question 6

A company has 200 small PyTorch models that are each used infrequently but need to be available for real-time inference. To minimize costs, they want to host all models on a single endpoint. Which SageMaker feature should they use?

Accepted Answer

Multi-model endpoint (MME). Multi-model endpoints allow hosting hundreds of models on a single endpoint, automatically loading/unloading models based on traffic. Multi-container endpoints are for different containers, not multiple models. Batch and asynchronous are not real-time.

Answer

Multi-container endpoint

Answer

Batch Transform job

Answer

Asynchronous inference endpoint

Question 7

A machine learning engineer deploys a new model version to a SageMaker endpoint with production variants. They want to gradually shift traffic from the old model to the new model, monitoring for errors, and automatically roll back if the error rate exceeds 5%. Which deployment pattern should they use?

Accepted Answer

Canary deployment with CloudWatch alarms. Canary deployments gradually shift traffic and allow automated rollback based on CloudWatch alarms. Blue/green switches all at once. A/B testing is for comparing variants. Shadow testing mirrors traffic but doesn't serve the new model to users.

Answer

A/B testing with traffic splitting

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Blue/green deployment

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Shadow testing

Question 8

A company needs to deploy a large language model (LLM) on SageMaker with the Triton Inference Server to maximize GPU utilization and reduce latency. They have an NVIDIA A100 GPU. Which SageMaker inference option supports Triton?

Accepted Answer

SageMaker real-time endpoint using a Triton Inference Server container. SageMaker real-time endpoints support the Triton Inference Server through a pre-built container that integrates with NVIDIA A100 GPUs, enabling dynamic batching and concurrent model execution to maximize GPU utilization and reduce latency. Triton is designed for high-throughput inference on GPU hardware, making it the correct choice for this scenario.

Answer

SageMaker Batch Transform with Triton

Answer

SageMaker Serverless Inference with a custom container

Answer

SageMaker Neo compiled model on a CPU endpoint

Question 9

A data scientist wants to version and manage trained models, require approval before deployment, and enable cross-account deployment. Which SageMaker feature provides these capabilities?

Accepted Answer

SageMaker Model Registry. SageMaker Model Registry is the correct choice because it provides a centralized catalog for versioning trained models, supports approval workflows (e.g., pending, approved, rejected) to gate deployment, and enables cross-account deployment by sharing model package ARNs across AWS accounts via AWS Resource Access Manager (RAM) or cross-account IAM roles. This directly satisfies all three requirements: versioning, approval before deployment, and cross-account deployment.

Answer

SageMaker Neo

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SageMaker Pipelines

Answer

Amazon Elastic Inference

Question 10

A company runs a batch inference job on 10 TB of image data stored in S3. Each image needs to be processed by a GPU-accelerated model. The job is not time-sensitive and cost is the primary concern. Which SageMaker option is MOST appropriate?

Accepted Answer

SageMaker Batch Transform with GPU instance and spot instances. Batch Transform with GPU spot instances is the most cost-effective choice for a non-time-sensitive, large-scale batch inference job on 10 TB of data. Spot instances offer up to 90% cost savings over on-demand, and Batch Transform natively handles splitting the dataset, distributing work across instances, and writing results to S3 without requiring a persistent endpoint.

Answer

SageMaker Serverless Inference

Answer

SageMaker Async Inference with GPU

Answer

SageMaker real-time endpoint on GPU instances

Question 11

A team uses SageMaker Pipelines to train and register a model. They want to conditionally run a hyperparameter tuning step only if the data quality check passes. Which pipeline step type should they use to branch the execution?

Accepted Answer

ConditionStep. The ConditionStep allows comparing values and branching to different steps. If data quality passes, the tuning step runs; otherwise, the pipeline stops or runs an alternative step. Other steps do not provide conditional branching.

Answer

TuningStep

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TrainingStep

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TransformStep

Question 12

An ML engineer needs to orchestrate a multi-step workflow that includes data preprocessing on Spark, model training on SageMaker, and deployment to a production endpoint. They require tight integration with other AWS services and the ability to add custom logic. Which AWS service should they use alongside SageMaker?

Accepted Answer

AWS Step Functions. AWS Step Functions is the correct choice because it provides a serverless workflow orchestration service that can coordinate multi-step ML pipelines involving Spark on AWS Glue or EMR, SageMaker training jobs, and endpoint deployments. It offers tight integration with over 200 AWS services via direct SDK integrations, supports custom logic through Lambda functions, and includes built-in error handling, retries, and parallel execution — making it ideal for complex, heterogeneous ML workflows that extend beyond SageMaker's native capabilities.

Answer

AWS CloudFormation

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SageMaker Pipelines

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Amazon EventBridge

Question 13

A machine learning team needs to deploy a PyTorch model that has been compiled with SageMaker Neo to improve inference performance on edge devices. Which TWO statements about SageMaker Neo are correct? (Select TWO.)

Accepted Answer

Neo reduces model inference latency through optimization techniques. SageMaker Neo optimizes models for specific hardware targets (e.g., ARM, Intel, NVIDIA) and reduces latency. It does not require training frameworks; it compiles trained models. It does not automatically scale endpoints. It is not limited to built-in algorithms.

Answer

Neo requires the model to be trained on SageMaker

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Neo can only compile models trained with SageMaker built-in algorithms

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Neo automatically scales SageMaker endpoints based on demand

Question 14

An ML engineer is designing a SageMaker Pipeline for model training and registration. They need to ensure that the pipeline can be re-run with different datasets without manual intervention, and that the steps are only re-executed if inputs have changed. Which THREE features should they configure? (Select THREE.)

Accepted Answer

Enable step caching to reuse outputs when inputs are unchanged. Pipeline parameters allow passing different inputs. Step caching reuses step outputs when inputs are identical. Using Parameterized execution is synonymous with using parameters. Lineage tracking is not for skipping steps. Condition steps are for branching, not caching. Model Registry is for versioning.

Answer

Add a Condition step to manually check for data changes

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Configure lineage tracking to record the origin of models

Question 15

A team is deploying a model using SageMaker real-time endpoint with an ml.m5.large instance. They notice high latency under peak load. They want to reduce latency without increasing instance size. Which THREE actions could help? (Select THREE.)

Accepted Answer

Quantize the model to reduce its size. SageMaker Neo compiles the model for the target hardware, reducing latency. Elastic Inference attaches GPU acceleration to a CPU instance. Model quantization reduces model size and speeds up inference. Increasing instance count does not reduce per-request latency (it increases throughput). Changing to a GPU instance increases instance size.

Answer

Increase the number of instances in the endpoint

Answer

Change the instance type to ml.g4dn.xlarge