MLA-C01 Deployment and Orchestration of ML Workflows — All Questions With Answers

Question 1mediummultiple choice

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An ML pipeline uses SageMaker Processing to run a feature engineering script. The script takes a long time and the team wants to speed up pipeline execution. What is the MOST effective approach?

Question 2mediummultiple choice

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A company uses SageMaker Model Registry to manage model versions. They want to automate the approval of models that pass automated evaluation, but require manual approval for others. Which Model Registry feature supports this workflow?

Question 3hardmulti select

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A team uses SageMaker Pipelines to train and evaluate a model. They want to run the training step only if the data quality check passes, otherwise skip. Which TWO pipeline step types are required? (Select TWO.)

Question 4easymultiple choice

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A machine learning engineer needs to deploy a model that requires less than 100 ms inference latency for real-time predictions. The model is a small PyTorch model that fits in a single GPU. Which SageMaker inference option is MOST cost-effective for this scenario?

Question 5mediummultiple choice

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A company has 200 small models (each ~100 MB) that serve different customers. They want to minimize costs while keeping low latency for each customer. Which SageMaker deployment approach is MOST suitable?

Question 6mediummultiple choice

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A team uses MLflow on SageMaker for experiment tracking. They want to automatically deploy the best-performing model from an MLflow run to a SageMaker endpoint for real-time inference. What is the MOST efficient way to achieve this?

Question 7mediummultiple choice

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A company uses SageMaker Model Registry to manage model versions. They have a cross-account deployment requirement: models approved in the development account must be deployed to a production account. Which approach is the MOST secure and recommended?

Question 8mediummultiple choice

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A team wants to use AWS Step Functions to orchestrate a retraining workflow that is triggered when new data arrives in an S3 bucket. They also need to monitor model drift. Which event-driven approach should they use?

Question 9easymultiple choice

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A company wants to deploy a PyTorch model that uses dynamic batching and model ensemble. They need to serve multiple models with different frameworks (PyTorch, TensorFlow) within the same endpoint. Which SageMaker feature should they use?

Question 10mediummulti select

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A data scientist needs to deploy an anomaly detection model that processes large payloads (up to 10 MB per request) and expects inference times of up to 10 minutes. The team wants to minimize cost and only pay per inference. Which TWO SageMaker inference options meet these requirements? (Choose TWO.)

Question 11hardmulti select

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A team is optimizing a deep learning model for deployment on SageMaker using SageMaker Neo. Which THREE of the following are valid optimization techniques that Neo can apply? (Choose THREE.)

Question 12mediummultiple choice

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A company wants to deploy a single model that processes images from a production line. The images are uploaded to an S3 bucket every few minutes, and the inference results must be stored back to S3. The team wants to avoid paying for idle compute and prefers a fully managed, on-demand solution. Which SageMaker inference option should they use?

Question 13hardmultiple choice

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A team has a large deep learning model that needs to be deployed for real-time inference with GPU acceleration. They want to use the Triton Inference Server on SageMaker to maximize throughput. Which instance type and configuration should they choose?

Question 14hardmulti select

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An ML engineer is designing a SageMaker Pipeline for a computer vision model. The pipeline includes steps for data processing, training, evaluation, and registration. The engineer wants to enable caching to avoid reprocessing when step inputs have not changed. For which steps is caching supported? (Select TWO.)

Question 15mediummulti select

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A company wants to use SageMaker to deploy a model that requires GPU acceleration for inference but wants to minimize costs by using a smaller attached GPU. Which options can they use? (Select TWO.)

Question 16mediummultiple choice

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A data science team needs to deploy a PyTorch model that performs real-time inference with sub-100ms latency. The model requires GPU acceleration, but the team wants to minimize cost by sharing GPU instances across multiple models. Which SageMaker hosting option should they choose?

Question 17mediummultiple choice

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A team wants to use MLflow on SageMaker to track experiments and manage model lifecycle. They need to register models in the SageMaker Model Registry after training. Which approach allows them to use MLflow for experiment tracking and then register the best model to SageMaker Model Registry?

Question 18mediummultiple choice

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A financial services company needs to enforce that only approved model versions are deployed to production. They use SageMaker Model Registry to track versions, with an approval workflow. Which action must they take in the model registry to ensure only approved models can be deployed?

Question 19mediummulti select

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A data science team uses SageMaker Pipelines to automate their ML workflow. They want to reduce costs by reusing outputs from previous pipeline runs when the input data and code have not changed. Which TWO actions should they take? (Choose two.)

Question 20hardmulti select

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An ML engineer needs to deploy a model that requires GPU acceleration but wants to reduce inference cost by optimizing the model. They are considering SageMaker Neo compilation and Amazon Elastic Inference. Which TWO statements are correct about these services? (Choose two.)

Question 21hardmultiple choice

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A team uses SageMaker Pipelines with a Condition step to decide whether to register a model based on evaluation metrics. They want to also store the evaluation results for lineage tracking. Which step should they use to record the metrics?

Question 22mediummultiple choice

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A team wants to implement an event-driven retraining pipeline that triggers retraining when new data arrives in an S3 bucket. The pipeline should include preprocessing, training, evaluation, and conditional registration. Which AWS services should they combine?

Question 23hardmulti select

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A machine learning engineer is designing a SageMaker Pipeline that includes a training step, a processing step for evaluation, and a condition step to decide whether to register the model. The pipeline should support caching to avoid redundant runs when inputs haven't changed. Which three steps must have caching enabled? (Select THREE.)

Question 24mediummulti select

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A team wants to deploy a single SageMaker real-time endpoint that serves both a PyTorch model for NLP and a TensorFlow model for image classification. Each model requires a different inference container. Which two features can they use together to achieve this? (Select TWO.)

Question 25hardmultiple choice

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A team uses SageMaker Pipelines to retrain a model nightly. They want to skip the training step if the new data is unchanged (same checksum as previous run) to save cost and time. Which pipeline configuration achieves this?

Question 26easymultiple choice

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A team wants to deploy a model that performs inference on large video files (up to 2 GB each) uploaded to an S3 bucket. The inference can tolerate a few minutes of latency. Which SageMaker inference option is most cost-effective?

Question 27mediummultiple choice

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A team wants to orchestrate a multi-step ML workflow that includes data preprocessing, hyperparameter tuning, model training, evaluation, and conditional deployment to staging or production based on evaluation metrics. The workflow should run on a schedule and track lineage. Which service should they use?

Question 28hardmultiple choice

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A team deploys a model on a SageMaker real-time endpoint using an ml.m5.xlarge instance. The model has high latency due to a large neural network. The team wants to reduce latency without changing the model code. Which option should they use?

Question 29mediummultiple choice

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A company uses SageMaker Model Registry to manage model versions. They want to enforce that only models with an 'Approved' status can be deployed to production endpoints. How can they enforce this?

Question 30hardmulti select

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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.)

Question 31mediummulti select

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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.)

Question 32mediummulti select

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A company wants to deploy a containerized inference application that includes a custom pre-processing script and a TensorFlow model on SageMaker. They need the ability to independently scale the pre-processing and model serving components. Which TWO SageMaker features support this? (Select TWO.)

Question 33hardmulti select

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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.)

Question 34mediummulti select

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A data science team is deploying a PyTorch model for real-time inference with sub-second latency requirements. They need to minimize cost while handling variable traffic. Which TWO approaches should they consider? (Choose TWO.)

Question 35mediummulti select

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A company wants to test a new ML model in production with minimal risk before shifting full traffic. They have an existing real-time endpoint serving model version A. They need to route 5% of live traffic to model version B and monitor performance for 24 hours. Which TWO steps should they take? (Choose TWO.)

Question 36easymultiple choice

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An ML engineer needs to deploy a scikit-learn model that must return predictions within 50 ms for a high-traffic web application. The model is small (< 100 MB) and the team wants fully managed infrastructure with automatic scaling. Which SageMaker deployment option should the engineer choose?

Question 37mediummultiple choice

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An ML engineer has registered a model in the SageMaker Model Registry. The model package group contains three approved model versions. The engineer needs to deploy the most recently approved version to a real-time endpoint using the SageMaker Python SDK. Which approach correctly resolves the latest approved model version and deploys it?

Question 38mediummultiple choice

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A data scientist is deploying a TensorFlow model to a SageMaker endpoint. The model requires custom Python preprocessing logic that must run in the same container as the model server. The team wants to package the inference code and dependencies into a single artifact for versioning and deployment. Which SageMaker feature should they use?

Question 39hardmultiple choice

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A team runs a real-time SageMaker endpoint backed by an auto scaling policy with a target of 70% InvocationsPerInstance. During a flash sale, traffic spikes far beyond the maximum instance count configured in the scaling policy. Requests begin to time out and the endpoint returns 429 errors. The team wants the endpoint to absorb short bursts without permanently raising the maximum instance count. Which change best addresses the immediate timeouts while preserving cost control?

Question 40mediummulti select

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A machine learning engineer needs to deploy a trained scikit-learn model to a SageMaker endpoint for real-time inference. The model artifacts are stored in Amazon S3. The engineer wants to ensure that the endpoint can scale automatically based on traffic and that the model is loaded efficiently. Which two actions should the engineer take? (Choose two.)

Question 41hardmultiple choice

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An ML engineer deploys a real-time SageMaker endpoint behind an Application Load Balancer. Traffic is bursty: for several minutes the endpoint receives almost no requests, then a large spike arrives. The engineer wants the endpoint to scale out quickly during spikes while avoiding unnecessary instance hours during quiet periods. Which configuration should the engineer apply?

Question 42mediummultiple choice

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A machine learning engineer needs to deploy a model to a SageMaker AI endpoint that will receive occasional large payloads (up to 1 GB) and can tolerate processing times of several minutes. The engineer wants to minimize cost by scaling the endpoint to zero instances when there are no requests. Which deployment option should the engineer use?

Question 43mediummultiple choice

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A machine learning engineer has deployed a real-time endpoint in Amazon SageMaker AI using a custom container. The model artifacts are stored in Amazon S3 and the container reads them at startup. The team wants to reduce cold-start latency for infrequent but latency-sensitive inference requests without changing the model or container image. Which SageMaker AI feature should the engineer use?

Question 44hardmulti select

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A machine learning engineer is deploying a model to a SageMaker AI endpoint and needs to ensure that the endpoint can handle sudden spikes in traffic without violating latency SLAs. The engineer wants to use automatic scaling and also implement a strategy to handle bursts. Which two actions should the engineer take? (Choose two.)

Question 45mediummulti select

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A company is deploying a large language model to a SageMaker real-time endpoint for an internal chatbot. Traffic is bursty, peak concurrency is high, and the team wants to reduce cost during idle periods while avoiding cold-start latency for interactive users. They also need the endpoint to scale out during bursts without manual intervention. (Choose two.)

Question 46hardmultiple choice

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An ML platform team deploys a model to a SageMaker real-time endpoint and configures a production variant with a data capture configuration that writes requests and responses to Amazon S3. Weeks later, a data scientist reports that captured files exist but contain only a subset of the fields the model actually received, and some requests appear missing entirely. The endpoint uses a custom inference container that the team built. What is the MOST likely cause?

Question 47hardmulti select

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A machine learning team wants to deploy the same trained model to multiple SageMaker AI real-time endpoints in different AWS Regions so that each endpoint serves local traffic with low latency. They want to avoid duplicating the model artifact in each Region manually. Which two actions should the engineer take to meet these requirements? (Choose two.)

Question 48mediummulti select

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A retail company runs a nightly retraining workflow built with SageMaker Pipelines. The pipeline includes a processing step that reads from an Amazon S3 input, a training step, and a model registration step. The team reports that the pipeline re-executes the processing step on every run even though the input data and the processing script have not changed, wasting time and cost. They want unchanged steps to be skipped on subsequent executions. (Choose two.)

Question 49easymultiple choice

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A data scientist needs to deploy a single ML model that will serve real-time predictions with low latency (under 10 ms) for a high-traffic web application. The model fits in memory and requires GPU acceleration. Which SageMaker inference option is MOST suitable?

Question 50mediummultiple choice

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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?

Question 51hardmultiple choice

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An ML team uses SageMaker Pipelines to automate model retraining. They want to skip redundant training steps when input data has not changed. Which feature should they enable?

Question 52mediummultiple choice

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A company needs to deploy a new model version to a SageMaker real-time endpoint. They want to route 5% of traffic to the new version initially to monitor for errors before full rollout. Which deployment strategy should they use?

Question 53easymultiple choice

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An ML engineer needs to compile a trained TensorFlow model to run efficiently on a target edge device with an ARM CPU. Which AWS service should they use?

Question 54mediummultiple choice

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A data science team uses SageMaker Pipelines for automated training. They need to conditionally register a model only if evaluation metrics exceed a threshold. Which pipeline step type should they use after the evaluation step?

Question 55hardmultiple choice

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A company wants to serve a large ensemble of models using NVIDIA Triton Inference Server on SageMaker for high throughput GPU inference. Which SageMaker inference option supports this?

Question 56easymultiple choice

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A company needs to deploy a model that processes large payloads (up to 1 GB) asynchronously. The results should be written to S3, and the team needs SNS notifications upon completion. Which SageMaker inference option is MOST suitable?

Question 57hardmultiple choice

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An ML team uses AWS Step Functions to orchestrate a retraining pipeline triggered by EventBridge when new training data arrives. The pipeline includes a SageMaker training job and a model evaluation. If evaluation fails, the team wants to send an alert. How should they implement this?

Question 58mediummultiple choice

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A startup wants to deploy a containerized ML application that includes both a model inference server and a preprocessing component in the same endpoint. Which SageMaker endpoint type supports running multiple containers?

Question 59mediummulti select

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A company uses SageMaker to deploy a model and wants to perform A/B testing by splitting traffic between two model variants. Which TWO actions should they take? (Select TWO.)

Question 60mediummulti select

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An organization wants to automate ML retraining using an event-driven architecture. Which THREE services should they combine? (Select THREE.)

Question 61mediummultiple choice

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A team built a SageMaker Pipeline that includes a training step and a model evaluation step. They want to automatically register a model in SageMaker Model Registry only if the evaluation metric (accuracy) exceeds 0.9. Which pipeline step should be used to implement this conditional logic?

Question 62hardmultiple choice

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A data science team uses SageMaker Pipelines to orchestrate their ML workflow. They noticed that even when source data hasn't changed, the pipeline re-runs all steps, wasting compute time. What should they enable to avoid redundant runs?

Question 63easymultiple choice

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A company wants to update an existing SageMaker real-time endpoint to serve a new model version. They need to route a small percentage of traffic to the new version initially and monitor for errors before switching fully. Which deployment pattern supports this?

Question 64hardmultiple choice

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A company deploys a large NLP model on a SageMaker real-time endpoint using an ml.p3.2xlarge instance. To reduce inference cost without sacrificing throughput, they want to compile the model for their target hardware. Which service should they use?

Question 65hardmultiple choice

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A team uses SageMaker real-time endpoints for inference. They want to deploy a new model version and compare its performance with the current version under live traffic without affecting user experience. Which method should they use?

Question 66mediummultiple choice

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A company wants to deploy a machine learning model using infrastructure as code to ensure reproducibility. They need to define the SageMaker Studio domain, user profiles, and the endpoint configuration. Which tool should they use?

Question 67mediummulti select

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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.)

Question 68mediummultiple choice

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A machine learning team has a model that needs to serve predictions with very low latency (under 10 ms) for a real-time web application. The model is a small ensemble of three neural networks that fits in memory. Which SageMaker inference option is MOST appropriate?

Question 69hardmultiple choice

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A data science team wants to host 50 different models for a recommendation engine. Each model is small (under 100 MB) and traffic patterns are unpredictable. They need to minimize cost and operational overhead. Which approach should they take?

Question 70easymultiple choice

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A machine learning engineer needs to deploy a new version of a model gradually, initially sending 5% of traffic to the new version and 95% to the current version, while monitoring for errors. Which deployment pattern should they use?

Question 71mediummultiple choice

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A company is using SageMaker Pipelines to orchestrate their ML workflow. They have a Condition step that checks if a model's accuracy exceeds 0.9. If true, they want to register the model in the model registry; otherwise, they want to run a retraining step. Which step type should they use for the decision?

Question 72mediummultiple choice

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An organization wants to ensure that only approved model versions can be deployed to production. They use the SageMaker Model Registry to track model versions. How can they enforce that only approved models are deployed?

Question 73easymultiple choice

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A company uses SageMaker Pipelines to automate their ML workflow. They notice that the pipeline reruns all steps even when the input data has not changed. Which feature should they enable to avoid unnecessary recomputation?

Question 74mediummultiple choice

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An ML engineer wants to use MLflow on SageMaker to track experiments and log metrics. They have set up MLflow on an EC2 instance. How can they best integrate MLflow tracking with SageMaker training jobs?

Question 75hardmultiple choice

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A company needs to update a model in production without any downtime. They currently have a single real-time endpoint serving traffic. Which approach allows them to deploy a new model version and switch traffic gradually while being able to roll back quickly?

Question 76easymultiple choice

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Which SageMaker feature compiles a trained model into an optimized binary for a specific hardware target (e.g., Intel, ARM, NVIDIA, or edge devices) to improve inference performance?

Question 77mediummultiple choice

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A team has a SageMaker Pipeline that trains a model and registers it in the Model Registry. They want to automate the deployment of the approved model to a staging environment. Which event-driven approach should they use?

Question 78mediummulti select

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A team is migrating their ML infrastructure to AWS and wants to use infrastructure as code to manage SageMaker Studio domains, user profiles, and associated resources. Which services can they use for this purpose? (Select THREE.)

Question 79easymultiple choice

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A machine learning engineer wants to automatically trigger a retraining pipeline whenever new training data arrives in an S3 bucket. The pipeline uses SageMaker Pipelines. Which AWS service should be used to detect the S3 event and start the pipeline?

Question 80hardmultiple choice

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A financial services company needs to deploy a machine learning model for real-time fraud detection. The model must be highly available across multiple Availability Zones and must support automatic scaling based on request volume. The company also needs to perform canary deployments to test new model versions with a small percentage of traffic before full rollout. Which SageMaker feature should they use?

Question 81easymultiple choice

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A company wants to deploy a trained XGBoost model for batch inference on a large dataset stored in S3. The inference job should be cost-effective and does not require real-time responses. Which SageMaker inference option should they use?

Question 82mediummultiple choice

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A machine learning engineer needs to deploy a TensorFlow model that requires a custom inference environment with specific system libraries. The model will be used in a real-time application with variable traffic. They want to minimize cold start latency. Which SageMaker hosting option should they choose?

Question 83hardmultiple choice

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A company uses SageMaker Pipelines to orchestrate their ML workflow. They notice that if a pipeline step fails due to a transient error (e.g., a brief network issue), the entire pipeline fails and they must manually rerun from the beginning. They want to automatically retry failed steps a few times before failing. What should they do?

Question 84easymultiple choice

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A data scientist wants to compare the performance of two model versions (V1 and V2) in production by splitting traffic between them. They want to gradually increase the percentage of traffic to the new version while monitoring metrics. Which SageMaker feature enables this?

Question 85mediummultiple choice

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A company is deploying a large NLP model on SageMaker for real-time inference. They want to reduce inference latency and cost by optimizing the model for the target hardware. The model is trained in PyTorch. Which SageMaker feature should they use to compile the model for best performance on the chosen instance?

Question 86hardmultiple choice

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A team needs to deploy a PyTorch model that uses custom CUDA kernels. They want to use NVIDIA Triton Inference Server on SageMaker for high-performance serving. Which SageMaker configuration is required to use Triton?

Question 87mediummultiple choice

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A company wants to deploy 50 small models (each ~100 MB) for real-time inference. They need to minimize hosting costs while maintaining low latency. Which SageMaker hosting option is most cost-effective?

Question 88mediummulti select

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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.)

Question 89easymultiple choice

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A data science team has trained a PyTorch model for real-time inference and needs to deploy it on AWS with GPU acceleration while minimizing cold-start latency. Which SageMaker inference option should they choose?

Question 90easymultiple choice

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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?

Question 91mediummultiple choice

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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?

Question 92mediummultiple choice

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A team needs to deploy a new model version to production while minimizing risk. They want to route 5% of live traffic to the new model and 95% to the current model, and then gradually increase the new model's traffic. Which SageMaker deployment pattern should they use?

Question 93mediummultiple choice

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A company wants to run inference on a large dataset stored in S3 using a pre-trained model. The inference can tolerate latency from minutes to hours, and they want a fully managed solution that autoscales to handle large volumes. Which SageMaker inference option is most suitable?

Question 94easymultiple choice

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A machine learning engineer needs to optimize a trained TensorFlow model for deployment on edge devices with limited compute. Which SageMaker feature should they use to compile the model for target hardware?

Question 95mediummultiple choice

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A company wants to deploy a PyTorch model on SageMaker using the NVIDIA Triton Inference Server for GPU acceleration. They have an existing Triton configuration. Which approach should they take?

Question 96hardmultiple choice

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A team needs to deploy a model that has compliance requirements to log all inference requests and responses for auditing. The model will be served using a real-time endpoint. How can they achieve this without custom code?

Question 97easymultiple choice

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A company wants to version and track ML models, with an approval workflow for promoting models from staging to production. Which SageMaker feature should they use?

Question 98mediummultiple choice

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A startup wants to deploy a model that has variable traffic patterns, with some periods of no traffic and occasional spikes. They want to pay only for what they use and do not want to manage instances. Which SageMaker inference option should they choose?

Question 99mediummulti select

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A company wants to deploy a new model using a canary deployment strategy on SageMaker. Which two actions should they take? (Select TWO.)

Question 100mediummultiple choice

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A company wants to serve 200 different PyTorch models. Each model is small (under 1 GB) and only a fraction are used at any time. To minimize cost and management overhead, which SageMaker inference option should be used?

Question 101mediummultiple choice

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A machine learning team needs to deploy a new model version for A/B testing, gradually shifting traffic from the old version to the new version over 24 hours. Which deployment strategy should they use?

Question 102mediummultiple choice

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A company needs to serve real-time predictions from a large ensemble of three deep learning models, each requiring different inference environments (PyTorch, TensorFlow, MXNet). Which SageMaker endpoint type supports running multiple inference containers together?

Question 103mediummultiple choice

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A data scientist wants to train a model on SageMaker using a custom PyTorch script, then register the best model in the SageMaker Model Registry. The training job is part of a SageMaker Pipeline. Which pipeline step should be used to register the model?

Question 104easymultiple choice

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A company uses SageMaker Neo to compile a trained model for deployment on edge devices. What is the primary benefit of using Neo?

Question 105mediummultiple choice

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A team uses MLflow on SageMaker for experiment tracking. They want to automate the retraining of a model when new training data arrives in an S3 bucket. Which combination of services should they use?

Question 106easymultiple choice

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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?

Question 107mediummulti select

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

Question 108easymultiple choice

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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?

Question 109mediummultiple choice

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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?

Question 110hardmultiple choice

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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?

Question 111mediummultiple choice

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A team uses SageMaker Pipelines to automate retraining. They want to skip the training step if the data has not changed since the last run. Which feature should they enable?

Question 112hardmultiple choice

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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?

Question 113easymultiple choice

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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?

Question 114mediummultiple choice

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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?

Question 115hardmultiple choice

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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?

Question 116mediummultiple choice

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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?

Question 117easymulti select

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A company wants to trigger a model retraining pipeline whenever new training data arrives in an S3 bucket. They also need to send a notification to a Slack channel when the retraining completes. Which TWO AWS services should they use to implement this event-driven workflow? (Select TWO.)

Question 118mediummulti select

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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.)

Question 119mediummulti select

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A company is using SageMaker to serve a model for real-time predictions. They want to test a new model version by routing a small percentage of live traffic to it while the rest goes to the current model. They also need to compare performance metrics. Which TWO actions should they take? (Select TWO.)

Question 120hardmulti select

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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.)

Question 121hardmulti select

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An MLOps team is designing a SageMaker Pipeline to automate model retraining. The pipeline must: (1) run training only if new training data is available, (2) register the model in SageMaker Model Registry only if evaluation metrics exceed a threshold, (3) deploy the approved model to a staging endpoint automatically. Which THREE steps should they include? (Choose THREE.)

Question 122mediummultiple choice

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A company runs a real-time fraud detection model on a SageMaker endpoint. The model is updated weekly, and each update must be validated against live traffic without affecting existing predictions. The team wants to compare the new model's performance against the current model using a small percentage of incoming requests, while ensuring that the current model continues to serve the majority of traffic. Which SageMaker deployment strategy should they use?

Question 123hardmultiple choice

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A machine learning engineer is using SageMaker Pipelines to orchestrate a training workflow. The pipeline includes a processing step that outputs a dataset, which is then used by a training step. The engineer notices that the processing step runs every time the pipeline executes, even when the input data has not changed. The engineer wants to avoid re-running the processing step if the input data and code are unchanged, while ensuring that downstream steps still execute if the processing step is skipped. Which approach should the engineer take?

Question 124mediummultiple choice

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A machine learning engineer has trained a scikit-learn model and saved it as model.joblib in Amazon S3. The engineer wants SageMaker to host the model for real-time inference without writing a custom container or inference script, because the model uses only standard predict behavior. Which deployment approach should the engineer use?

Question 125mediummultiple choice

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A machine learning engineer is deploying a real-time inference endpoint on Amazon SageMaker AI for a fraud detection model. The model must serve predictions with consistent latency under 50 ms and the team expects traffic to fluctuate unpredictably, with occasional bursts. The engineer wants to automatically adjust the number of instances based on actual workload while minimizing cost during idle periods. Which SageMaker AI feature should the engineer configure?

Question 126hardmulti select

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A machine learning team is deploying a model to a SageMaker endpoint and needs to implement A/B testing between two model versions. They want to split traffic 80/20 and monitor performance metrics for each variant. Which two actions should they take? (Choose two.)

Question 127mediummultiple choice

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A machine learning engineer is deploying a model to a SageMaker endpoint that must handle occasional large payloads up to 1 GB. The inference time can take up to 10 minutes. The team wants to minimize cost and avoid idle compute. Which deployment option is most appropriate?

Question 128mediummultiple choice

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An ML engineer has a real-time SageMaker endpoint serving a fraud-detection model. The team wants to release a new model version to a small percentage of live traffic first, monitor CloudWatch metrics for accuracy regressions, and roll back quickly if performance degrades. They also want the production and candidate variants to share the same endpoint so latency comparisons are apples-to-apples. Which SageMaker deployment strategy should they use?

Question 129easymultiple choice

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A machine learning engineer has trained a model using SageMaker and wants to deploy it to a real-time endpoint. The engineer needs to specify the model artifacts, the inference code, and the environment. Which SageMaker resource should the engineer create first?

Question 130hardmultiple choice

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A company is deploying a large language model (LLM) to a SageMaker endpoint. They want to minimize inference latency and cost by using GPU acceleration and model parallelism. The model is too large to fit on a single GPU. Which SageMaker feature should they use?

Question 131hardmultiple choice

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A team runs a SageMaker Pipeline that trains a model and registers it in the Model Registry. Compliance requires that the pipeline run automatically every time new labeled data lands in S3, and that each run record the exact S3 data prefix, the training image URI, and the git commit hash as lineage metadata. The engineer wants the least operational overhead. Which approach meets these requirements?

Question 132mediummultiple choice

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A data science team is using AWS Step Functions to orchestrate a machine learning workflow that includes a SageMaker training job followed by a model deployment. They want to ensure that if the training job fails, the workflow retries up to three times with exponential backoff before sending a notification to an Amazon SNS topic. Which Step Functions feature should they use to implement this?

Question 133hardmulti select

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A team is building a SageMaker Pipeline that trains a model and then registers it in the SageMaker Model Registry. They want the pipeline to automatically deploy the model to a real-time endpoint only after a human approves the model package. Which two actions should the team take to implement this approval-gated deployment? (Choose two.)

Question 134hardmultiple choice

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A machine learning team runs a SageMaker AI Pipeline that trains a model and registers it in the SageMaker AI Model Registry. A separate deployment process must promote the model to production only after a human reviewer approves the model version. The team wants to automate the promotion so that approval in the Model Registry triggers the deployment without manual intervention. Which combination of steps should the engineer implement?

Question 135mediummultiple choice

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A media company runs a real-time recommendation model on a SageMaker endpoint. Traffic triples every evening between 18:00 and 22:00 and drops to near zero overnight, and the team wants to cut costs without underserving evening users. They want the endpoint to scale out automatically as invocations rise and scale back in when traffic falls. Which solution should they implement?

Question 136easymultiple choice

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A machine learning engineer needs to run a one-time scoring job over 500 GB of data stored in Amazon S3 using a trained model, and the results must be written back to S3. There is no requirement for a persistent HTTPS endpoint. Which SageMaker feature should the engineer use?

Question 137mediummultiple choice

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A company wants to deploy a scikit-learn model to a SageMaker AI real-time endpoint. The model must be loaded from a custom Python module that contains preprocessing logic not present in the built-in scikit-learn container. The team wants to minimize operational overhead and does not need to change system-level libraries. Which approach should the engineer take?

Question 138hardmultiple choice

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An ML platform team must orchestrate a workflow that trains a model, evaluates it against a baseline, and only registers the model if evaluation passes. If evaluation fails, the workflow must notify the data science channel and stop without registering. The team wants the orchestration logic to be expressed as code, versioned in git, and integrated with SageMaker training jobs and Model Registry. Which approach best fits these requirements?

Question 139easymultiple choice

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An ML engineer has trained a model and stored the model artifacts in an Amazon S3 bucket in the same AWS Region as the planned SageMaker AI endpoint. During endpoint creation, the engineer must specify the S3 location of the model artifacts. Which permission must the SageMaker AI execution role have for the endpoint to load the model successfully?

Question 140mediummultiple choice

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A retailer runs a nightly batch scoring job that processes 40 GB of transaction data and writes predictions to S3. Occasionally a single partition is corrupt, causing the entire job to fail after several hours. The team wants the job to skip the corrupt partition, log which partition failed, and still complete processing of the remaining data with minimal changes to their existing SageMaker Processing job. Which change should they make?

Question 141mediummultiple choice

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A fraud-detection team trains a model in SageMaker and wants to shift 10 percent of live prediction traffic to a newly retrained model to compare accuracy before a full cutover. The endpoint already serves the current model on one production variant. They need the endpoint to route a controlled fraction of requests to the new model without changing the client application. What should they do?

Question 142easymultiple choice

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A hospital's ML team deploys a diagnostic model to a SageMaker real-time endpoint. Compliance requires that every inference request and response be recorded for later auditing, and the records must be retrievable months later. The team needs a low-effort way to capture this data. Which approach should they use?