CCNA Deployment and Orchestration of ML Workflows Questions — Page 2 of 2

MCQmedium

A team has 200 small ML models that need to be served via HTTPS endpoints. Each model is used infrequently, and the team wants to minimize hosting costs. Which SageMaker deployment approach is MOST cost-effective?

A.Use SageMaker Serverless Inference for each model

B.Deploy each model on a separate real-time endpoint

C.Use Batch Transform for all models

D.Use a single multi-model endpoint (MME)

AnswerD

MME dynamically loads models from Amazon S3 onto shared instances, minimizing cost for many infrequently used models.

Why this answer

Multi-model endpoints (MME) allow hosting multiple models on a single endpoint, sharing instances and reducing costs, especially for infrequently used models.

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MCQmedium

A machine learning team uses SageMaker Pipelines to automate retraining. They want to avoid re-running data processing steps if the data has not changed since the last successful pipeline run. Which built-in feature should they enable?

A.Pipeline caching

B.Model lineage tracking

C.Parameterized pipeline executions

D.Step parallelism

AnswerA

Caching reuses step outputs when inputs and configuration haven't changed, avoiding redundant processing.

Why this answer

Pipeline caching is the correct choice because SageMaker Pipelines can cache the outputs of each step based on a hash of the step's input parameters, configuration, and code. If the hash matches a previous successful run, the cached output is reused, avoiding redundant execution of data processing steps when the underlying data hasn't changed.

Exam trap

The trap here is that candidates confuse lineage tracking (Option B) with caching, assuming that tracking data versions automatically prevents re-execution, when in fact lineage only records history without affecting pipeline execution behavior.

How to eliminate wrong answers

Option B is wrong because model lineage tracking (via SageMaker ML Lineage Tracking) records the relationships between data, models, and training jobs, but it does not prevent re-running steps; it only provides auditability and provenance. Option C is wrong because parameterized pipeline executions allow you to pass different input values at runtime, but they do not automatically skip unchanged steps—caching is required for that. Option D is wrong because step parallelism controls the concurrency of step execution within a pipeline, not the reuse of previous outputs.

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Multi-Selectmedium

A company is using AWS Step Functions to orchestrate their ML retraining pipeline. They want to trigger retraining when new data arrives, but only if the model's performance has degraded below a threshold. Which THREE AWS services should they use together to achieve this? (Choose three.)

Select 3 answers

A.AWS Step Functions

B.AWS Lambda

C.Amazon EventBridge

D.Amazon CloudWatch Logs

E.SageMaker Model Registry

AnswersA, B, C

Step Functions orchestrates the retraining pipeline.

Why this answer

A solution: Amazon EventBridge detects S3 events (new data), invokes a Lambda function that checks model performance (e.g., via SageMaker Model Monitor or custom metrics), and then starts a Step Functions workflow if degradation is detected. The other services: SageMaker Pipelines could replace Step Functions but is not listed as an option; SageMaker Model Monitor can track performance but is not an event source; CloudWatch Logs is not directly involved in the trigger logic.

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Multi-Selectmedium

A company uses SageMaker Pipelines to automate their ML workflow. They need to add model versioning and approval workflow. Which THREE steps should they include in their pipeline to achieve this? (Choose THREE.)

Select 3 answers

A.RegisterModel step

B.Training step

C.Condition step

D.Processing step for evaluation

E.Transform step

AnswersA, C, D

This step creates a new model version in the Model Registry.

Why this answer

The RegisterModel step is correct because it creates a model package in SageMaker Model Registry, which enables versioning and approval workflows. This step registers the trained model artifact along with metadata, allowing the pipeline to track model versions and trigger approval processes for deployment.

Exam trap

The trap here is that candidates may think the Training step alone suffices for versioning, but AWS explicitly separates model training from model registration, requiring the RegisterModel step for registry integration.

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MCQeasy

A company has 50 small PyTorch models that are used infrequently for inference. They want to minimize costs while maintaining the ability to serve all models from a single endpoint. Which SageMaker feature should they use?

A.Multi-container endpoint

B.Batch transform job

C.Real-time endpoint with 50 production variants

D.Multi-model endpoint

AnswerD

MME hosts many models on one endpoint, loading each model on demand. Ideal for many small, infrequently used models.

Why this answer

Multi-model endpoints (MME) allow hosting multiple models on a single endpoint, loading models dynamically based on the target model in the request. This reduces cost for many small, infrequently used models by sharing the underlying instance.

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Multi-Selectmedium

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.)

Select 2 answers

A.Neo reduces model inference latency through optimization techniques

B.Neo requires the model to be trained on SageMaker

C.Neo compiles models for a specific hardware target, such as Intel or ARM

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

E.Neo automatically scales SageMaker endpoints based on demand

AnswersA, C

Why this answer

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.

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