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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: ML Model Development | 26% | - Evaluate and analyze model performance
|
| Topic 2: Deployment and Orchestration of ML Workflows | 22% | - Configure deployment for scalability and availability
|
| Topic 3: ML Solution Monitoring, Maintenance, and Security | 24% | - Monitor model and data quality
|
| Topic 4: Data Preparation for Machine Learning | 28% | - Ingest and store data
|
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NEW QUESTION # 13
A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account.
An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses.
Which solution will meet these requirements?
Answer: A
NEW QUESTION # 14
A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold.
Which solution will meet these requirements?
Answer: C
NEW QUESTION # 15
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
* An S3 event notification invokes the pipeline when new data is uploaded.
* S3 Lifecycle rule invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
* The pipeline deploys the model to SageMaker Model Registry.
Answer:
Explanation:
Explanation:
Step 1: An S3 event notification invokes the pipeline when new data is uploaded.
Step 2: SageMaker retrains the model by using the data in the S3 bucket.
Step 3: The pipeline deploys the model to a SageMaker endpoint.
* Step 1: An S3 Event Notification Invokes the Pipeline When New Data is Uploaded
* Why? The CI/CD pipeline should be triggered automatically whenever new training data is uploaded to Amazon S3. S3 event notifications can be configured to send events to AWS services like Lambda, which can then invoke AWS CodePipeline.
* How? Configure the S3 bucket to send event notifications (e.g., s3:ObjectCreated:*) to AWS Lambda, which in turn triggers the CodePipeline.
* Step 2: SageMaker Retrains the Model by Using the Data in the S3 Bucket
* Why? The uploaded data is used to retrain the ML model to incorporate new information and maintain performance. This step is critical to updating the model with fresh data.
* How? Define a SageMaker training step in the CI/CD pipeline, which reads the training data from the S3 bucket and retrains the model.
* Step 3: The Pipeline Deploys the Model to a SageMaker Endpoint
* Why? Once retrained, the updated model must be deployed to a SageMaker endpoint to make it available for real-time inference.
* How? Add a deployment step in the CI/CD pipeline, which automates the creation or update of the SageMaker endpoint with the retrained model.
Order Summary:
* An S3 event notification invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
This configuration ensures an automated, efficient, and scalable CI/CD pipeline for continuous retraining and deployment of the ML model in Amazon SageMaker.
NEW QUESTION # 16
A company has built more than 50 models and deployed the models on Amazon SageMaker Al as real-time inference endpoints. The company needs to reduce the costs of the SageMaker Al inference endpoints. The company used the same ML framework to build the models. The company ' s customers require low-latency access to the models.
Select and order the correct steps from the following list to reduce the cost of inference and keep latency low.
Select each
step one time or not at all. (Select and order FIVE.)
Create an endpoint configuration that references a multi-model container.
. Create a SageMaker Al model with multi-model endpoints enabled.
. Deploy a real-time inference endpoint by using the endpoint configuration.
. Deploy a serverless inference endpoint configuration by using the endpoint configuration.
Spread the existing models to multiple different Amazon S3 bucket paths.
. Upload the existing models to the same Amazon S3 bucket path.
. Update the models to use the new endpoint ID. Pass the model IDs to the new endpoint.
Answer:
Explanation:
Explanation:
Step 1
Upload the existing models to the same Amazon S3 bucket path.
Multi-model endpoints require all models to be stored under a single S3 prefix so SageMaker can dynamically load them on demand.
Step 2
Create a SageMaker AI model with multi-model endpoints enabled.
This creates a SageMaker model resource that uses a multi-model-capable container (for example, XGBoost, PyTorch, or TensorFlow MME-compatible containers).
Step 3
Create an endpoint configuration that references a multi-model container.
The endpoint configuration defines:
Instance type
Initial instance count
The multi-model container reference
Step 4
Deploy a real-time inference endpoint by using the endpoint configuration.
Real-time endpoints ensure low-latency inference, which is a strict customer requirement.
Step 5
Update the models to use the new endpoint ID. Pass the model IDs to the new endpoint.
Each inference request specifies a model ID so SageMaker knows which model to load from S3.
NEW QUESTION # 17
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?
Answer: A
Explanation:
Recall measures the ability of a model to correctly identify all positive cases (true positives) out of all actual positives, minimizing false negatives. Since the cost of false negatives is much higher than falsepositives in this scenario, the ML engineer should prioritize models with high recall to reduce the likelihood of missing positive cases.
NEW QUESTION # 18
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