Home › Certifications › AWS Certified Machine Learning Engineer MLA-C01 › Deployment and Orchestration of ML Workflows
50 multiple-choice questions and 10 flashcards on Deployment and Orchestration of ML Workflows, about 24% of the AWS Certified Machine Learning Engineer MLA-C01 bank. Every one carries a written rationale.
Deployment and Orchestration of ML Workflows is one of 4 chapters in CoStudy's AWS Certified Machine Learning Engineer — Associate (MLA-C01) bank, and it holds 50 of the bank's 210 multiple-choice questions — roughly 24% of the total. That proportion is not arbitrary: chapters follow the certifying body's published exam outline, and the number of questions in each is set by that domain's published weight, so the share of your practice time this chapter takes matches the share of the real exam it accounts for.
Studying by chapter is worth doing once you have a diagnostic score. A single overall percentage tells you whether you are close; it does not tell you which domain is dragging. Working a weak chapter in isolation, and re-testing it in isolation, is the fastest way to move a score that has stalled — and it is why the mock exams in CoStudy report by domain rather than as one number.
10 questions drawn from this chapter, with the full rationale shown — the controlling principle behind the right answer, and why each wrong option tempts and fails.
Inference Recommender MOST DIRECTLY provides which value?
Answer: C — Optimal instance-type suggestion
Inference Recommender load-tests instance types to suggest best cost/performance; the others are different features.
An endpoint receives bursty traffic with long idle gaps between invocations, and cold-start latency of a few seconds is acceptable. Which SageMaker hosting option minimizes idle cost?
Answer: A — Serverless Inference endpoint
Serverless Inference scales to zero between invocations, ideal for bursty/idle-heavy traffic; a real-time endpoint sized for peak pays for idle capacity constantly; Batch Transform can't serve live per-request calls; MME with reserved instances still bills for always-on capacity.
For very low-latency inference on custom silicon, MOST fit family is which?
Answer: C — inf2 Inferentia instances
Inferentia is inference-optimized silicon; CPUs are slow; p3 is costly for inference.
Two different frameworks (a preprocessing container and an XGBoost container) must run as one inference call in sequence, with SageMaker managing invocation order. What SageMaker feature is this?
Answer: D — Multi-Container Endpoint (MCE) with an inference pipeline
MCE (inference pipeline) chains multiple containers so SageMaker passes output from one to the next in a single invocation; MME hosts many independent single-purpose models, not a chained sequence; ProcessingStep is a pipeline build-time step, not live inference chaining; production variants split traffic between competing models, not chain containers.
Limit endpoint invocation to specific principals via which control?
Answer: A — Endpoint policy with IAM
Endpoint IAM policies control InvokeEndpoint principals; S3, DNS, and public URLs do not gate access.
SageMaker Clarify PRIMARILY reports which pair of insights?
Answer: A — Bias and explainability results by design
Clarify surfaces bias metrics and SHAP explainability; the others are unrelated to Clarify.
A regulated pipeline must run data validation, training, evaluation, and a human-approval gate as one reproducible, versioned DAG triggered by CI/CD, with lineage tracked automatically. Which service defines the DAG?
Answer: C — SageMaker Pipelines
SageMaker Pipelines is purpose-built for ML DAGs with native SageMaker Lineage tracking and Model Registry integration; Step Functions and MWAA can orchestrate ML steps too but lack SageMaker's native lineage/registry integration out of the box; Glue Workflows orchestrates Glue ETL jobs, not full ML training/eval/approval DAGs.
A pipeline's evaluation step must automatically decide whether to proceed to RegisterModelStep based on whether the trained model's AUC exceeds a threshold. Which SageMaker Pipelines construct expresses this branch?
Answer: C — A ConditionStep evaluating the metric
ConditionStep is the native SageMaker Pipelines construct for branching on a computed metric such as AUC; a parallel TrainingStep doesn't evaluate conditions; polling S3 via EventBridge is an indirect, unnecessary detour for an in-pipeline decision; a CodePipeline manual gate requires a human, not an automatic metric check.
For A/B testing endpoints, MOST fit SageMaker feature is which?
Answer: C — Production variants weights
Production variants split traffic between models for A/B; the others do not.
A team wants merges to the main branch of their model-training repo to automatically trigger data validation, training, evaluation, and conditional deployment with no manual pipeline authoring from scratch. Which AWS offering provisions this MLOps scaffold?
Answer: A — SageMaker Projects with the built-in MLOps template
SageMaker Projects creates the CodeCommit repo, CodePipeline, and CodeBuild wiring in one step via a built-in MLOps template; CodeBuild alone runs commands but has no orchestration; hand-building Step Functions works but isn't the managed scaffold the question asks for; SSM Automation targets infrastructure runbooks, not ML pipeline provisioning.
4 cards from the 10 in this chapter.
ML workflow CI/CD on AWS — services?
CodeCommit/GitHub + CodeBuild + CodePipeline + SageMaker Pipelines + Model Registry.
SageMaker Inference Recommender?
Benchmarks candidate instance types/configs against a trained model and recommends the best cost/latency fit before production deployment.
SageMaker deployment options?
Real-time endpoint, Asynchronous Inference, Batch Transform, Serverless Inference, Multi-Model Endpoints.
SageMaker Deployment Guardrails?
Built-in traffic-shifting safety net for endpoint updates — canary or linear traffic shift with CloudWatch alarm-based automatic rollback.
These are a sample. The full Deployment and Orchestration of ML Workflows chapter runs 60 items with per-chapter progress tracking, on the web and in the iOS app.
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