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| Section | Weight | Objectives |
|---|---|---|
| ML Solution Monitoring, Maintenance, and Security | 24% | - Monitoring and observability
|
| Deployment and Orchestration of ML Workflows | 22% | - ML pipeline orchestration
|
| ML Model Development | 26% | - Model selection and training
|
| Data Preparation for Machine Learning (ML) | 28% | - Data ingestion and collection
|
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NEW QUESTION # 230
A company is planning to create an internal-only chat interface to help employees handle customer queries. Currently, the employees need to refer to a massive knowledge base of internal documents to address customer issues. The new solution must be serverless. Which combination of steps will meet these requirements?
Answer: B,D,E
Explanation:
To build a serverless internal chat interface, you can use Amazon Bedrock with a foundation model like Claude, invoke the model API through AWS Lambda with Amazon API Gateway, and store embeddings in a vector database format using Amazon S3. This avoids server management, ensures scalability, and leverages serverless components end-to-end.
NEW QUESTION # 231
An ML engineer is using Amazon SageMaker Canvas to build a custom ML model from an imported dataset.
The model must make continuous numeric predictions based on 10 years of data.
Which metric should the ML engineer use to evaluate the model's performance?
Answer: C
Explanation:
This is a regression problem, where the target variable is continuous and numeric. AWS documentation clearly states that classification metrics such as accuracy and AUC are not appropriate for regression models.
Root Mean Square Error (RMSE) measures the square root of the average squared differences between predicted and actual values. RMSE penalizes larger errors more heavily, making it especially useful when large prediction errors are costly or undesirable.
SageMaker Canvas automatically selects regression metrics such as RMSE and MAE when building regression models. RMSE is widely used for time-based and numeric prediction problems, especially when evaluating long historical datasets.
Inference latency measures system performance, not model accuracy.
Therefore, Option D is the correct and AWS-verified answer.
NEW QUESTION # 232
A gaming company needs to deploy a natural language processing (NLP) model to moderate a chat forum in a game. The workload experiences heavy usage during evenings and weekends but minimal activity during other hours.
Which solution will meet these requirements MOST cost-effectively?
Answer: B
Explanation:
The key requirements in this scenario are variable traffic patterns and cost efficiency. The workload has unpredictable spikes during evenings and weekends, followed by long periods of low or no usage. According to AWS Machine Learning documentation, Amazon SageMaker Serverless Inference is specifically designed for such use cases.
SageMaker Serverless Inference automatically provisions, scales, and shuts down compute resources based on incoming inference requests. Customers are billed only for the compute time used during inference, not for idle resources. This makes it highly cost-effective for workloads with intermittent or spiky traffic, such as real- time chat moderation in gaming environments.
Option A is incorrect because batch transform jobs are intended for offline, large-scale inference and require fixed capacity during job execution. They are not suitable for real-time NLP moderation.
Option C is also incorrect because reserving an EC2 GPU instance incurs continuous costs regardless of utilization. This would be inefficient given the long idle periods described in the scenario.
Option D, SageMaker Asynchronous Inference, is designed for workloads with long processing times or large payloads and still requires endpoint provisioning. While it can handle traffic spikes, it does not scale down to zero in the same cost-efficient manner as Serverless Inference.
Therefore, Amazon SageMaker Serverless Inference is the most cost-effective and operationally efficient solution for deploying an NLP moderation model with highly variable usage patterns.
NEW QUESTION # 233
A company needs to update the model definition of an existing Amazon SageMaker Al endpoint.
Select and order the correct steps from the following list to update the model definition settings with the LEAST interruption of inferences. Select each step one time or not at all. (Select and order THREE.)
* Create a new endpoint configuration that uses the new model definition.
* Create a new model definition with updated settings by using the CreateModel action in the SageMaker AI API.
* Delete the endpoint that needs to be updated and recreate the endpoint with the new endpoint configuration.
* Delete the IAM role and permissions for the ExecutionRoleArn parameter.
* Update the endpoint with the new endpoint configuration.
Answer:
Explanation:
Explanation:
Step 1: Create a new model definition with updated settings by using the CreateModel action in the SageMaker AI API.
Step 2: Create a new endpoint configuration that uses the new model definition.
Step 3: Update the endpoint with the new endpoint configuration.
Do not delete and recreate the endpoint. That causes unnecessary inference interruption. SageMaker endpoint updates are designed to use a new EndpointConfig; AWS explicitly states that to update an endpoint, you must create a new endpoint configuration, then call UpdateEndpoint on the existing endpoint. During the update, SageMaker changes the endpoint to Updating and then back to InService.
NEW QUESTION # 234
A company wants to improve the sustainability of its ML operations.
Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)
Answer: B,D
NEW QUESTION # 235
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