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| Section | Weight | Objectives |
|---|---|---|
| Data Preparation for Machine Learning | 28% | - Ensure data integrity and prepare for modeling
|
| ML Solution Monitoring, Maintenance, and Security | 24% | - Optimize and maintain workloads
|
| ML Model Development | 26% | - Train, tune, and refine models
|
| Deployment and Orchestration of ML Workflows | 22% | - Choose deployment infrastructure and pattern
|
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NEW QUESTION # 182
A company has developed a new ML model. The company requires online model validation on 10% of the traffic before the company fully releases the model in production. The company uses an Amazon SageMaker endpoint behind an Application Load Balancer (ALB) to serve the model.
Which solution will set up the required online validation with the LEAST operational overhead?
Answer: B
Explanation:
Scenario: The company wants to perform online validation of a new ML model on 10% of the traffic before fully deploying the model in production. The setup must have minimal operational overhead.
Why Use SageMaker Production Variants?
* Built-In Traffic Splitting: Amazon SageMaker endpoints support production variants, allowing multiple models to run on a single endpoint. You can direct a percentage of incoming traffic to each variant by adjusting the variant weights.
* Ease of Management: Using production variants eliminates the need for additional infrastructure like separate endpoints or custom ALB configurations.
* Monitoring with CloudWatch: SageMaker automatically integrates with CloudWatch, enabling real- time monitoring of model performance and invocation metrics.
Steps to Implement:
* Deploy the New Model as a Production Variant:
* Update the existing SageMaker endpoint to include the new model as a production variant. This can be done via the SageMaker console, CLI, or SDK.
Example SDK Code:
import boto3
sm_client = boto3.client('sagemaker')
response = sm_client.update_endpoint_weights_and_capacities(
EndpointName='existing-endpoint-name',
DesiredWeightsAndCapacities=[
{'VariantName': 'current-model', 'DesiredWeight': 0.9},
{'VariantName': 'new-model', 'DesiredWeight': 0.1}
]
)
* Set the Variant Weight:
* Assign a weight of 0.1 to the new model and 0.9 to the existing model. This ensures 10% of traffic goes to the new model while the remaining 90% continues to use the current model.
* Monitor the Performance:
* Use Amazon CloudWatch metrics, such as InvocationCount and ModelLatency, to monitor the traffic and performance of each variant.
* Validate the Results:
* Analyze the performance of the new model based on metrics like accuracy, latency, and failure rates.
Why Not the Other Options?
* Option B: Setting the weight to 1 directs all traffic to the new model, which does not meet the requirement of splitting traffic for validation.
* Option C: Creating a new endpoint introduces additional operational overhead for traffic routing and monitoring, which is unnecessary given SageMaker's built-in production variant capability.
* Option D: Configuring the ALB to route traffic requires manual setup and lacks SageMaker's seamless variant monitoring and traffic splitting features.
Conclusion:
Using production variants with a weight of 0.1 for the new model on the existing SageMaker endpoint provides the required traffic split for online validation with minimal operational overhead.
References:
Amazon SageMaker Endpoints
SageMaker Production Variants
Monitoring SageMaker Endpoints with CloudWatch
NEW QUESTION # 183
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?
Answer: B
NEW QUESTION # 184
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company is experimenting with consecutive training jobs.
How can the company MINIMIZE infrastructure startup times for these jobs?
Answer: B
Explanation:
When running consecutive training jobs in Amazon SageMaker, infrastructure provisioning can introduce latency, as each job typically requires the allocation and setup of compute resources. To minimize this startup time and enhance efficiency, Amazon SageMaker offers Managed Warm Pools.
Key Features of Managed Warm Pools:
Reduced Latency: Reusing existing infrastructure significantly reduces startup time for training jobs.
Configurable Retention Period: Allows retention of resources after training jobs complete, defined by the KeepAlivePeriodInSeconds parameter.
Automatic Matching: Subsequent jobs with matching configurations (e.g., instance type) can reuse retained infrastructure.
Implementation Steps:
Request Warm Pool Quota Increase: Increase the default resource quota for warm pools through AWS Service Quotas.
Configure Training Jobs:
Set KeepAlivePeriodInSeconds for the first training job to retain resources.
Ensure subsequent jobs match the retained pool ' s configuration to enable reuse.
Monitor Warm Pool Usage: Track warm pool status through the SageMaker console or API to confirm resource reuse.
Considerations:
Billing: Resources in warm pools are billable during the retention period.
Matching Requirements: Jobs must have consistent configurations to use warm pools effectively.
Alternative Options:
Managed Spot Training: Reduces costs by using spare capacity but doesn't address startup latency.
SageMaker Training Compiler: Optimizes training time but not infrastructure setup.
SageMaker Distributed Data Parallelism Library: Enhances training efficiency but doesn't reduce setup time.
By using Managed Warm Pools, the company can significantly reduce startup latency for consecutive training jobs, ensuring faster experimentation cycles with minimal operational overhead.
AWS Documentation: Managed Warm Pools
AWS Blog: Reduce ML Model Training Job Startup Time
NEW QUESTION # 185
A company is building an Amazon SageMaker AI pipeline for an ML model. The pipeline uses distributed processing and distributed training.
An ML engineer needs to encrypt network communication between instances that run distributed jobs. The ML engineer configures the distributed jobs to run in a private VPC.
What should the ML engineer do to meet the encryption requirement?
Answer: A
Explanation:
In Amazon SageMaker, distributed training and distributed processing jobs often involve multiple instances exchanging data over the network. By default, when these jobs run inside a VPC, network traffic remains private but is not automatically encrypted between instances. When compliance or security requirements mandate encryption of in-transit data, additional configuration is required.
The correct solution is to enable inter-container traffic encryption, which ensures that all network communication between containers running on different instances is encrypted using TLS. Amazon SageMaker provides a built-in feature for this purpose. When inter-container traffic encryption is enabled, SageMaker automatically configures secure communication channels between all nodes participating in a distributed job, including training clusters and processing jobs.
Option A (Network isolation) is incorrect because network isolation prevents containers from making outbound network calls and accessing the internet. It does not encrypt traffic between instances.
Option B (Security groups) is incorrect because security groups control network access and traffic flow, not encryption. They can restrict which instances can communicate, but they do not provide data-in-transit encryption.
Option D (VPC flow logs) is incorrect because VPC flow logs are used for monitoring and auditing network traffic, not for encrypting it.
AWS documentation explicitly states that enabling inter-container traffic encryption is the recommended and supported approach for encrypting data exchanged between instances during distributed SageMaker jobs. This feature aligns with enterprise security best practices and regulatory requirements for protecting sensitive ML training data in transit.
Therefore, Option C is the only solution that directly fulfills the encryption requirement for distributed SageMaker workloads.
NEW QUESTION # 186
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 # 187
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