Pass the Amazon Web Services AWS Certified Associate MLA-C01 Questions and answers with CertsForce

Partitioning the time-series data by date prefix in the S3 bucket significantly improves query performance in Amazon Athena by reducing the amount of data that needs to be scanned during queries. This allows the ML engineers to efficiently analyze trends over specific time periods, such as the past 3 days. Applying S3 Lifecycle policies to archive partitions older than 30 days to S3 Glacier Flexible Retrieval ensures cost-effective data retention and storage management while maintaining high performance for recent data retrieval.

This use case involves large payloads (high-resolution images), variable processing time, and a requirement to notify users upon completion. AWS documentation recommends Amazon SageMaker asynchronous inference for exactly this scenario.

Asynchronous inference allows clients to submit inference requests without waiting for a response. The request payload is stored in Amazon S3, and the inference result is written back to S3 when processing is complete. This approach is designed for workloads that cannot meet real-time latency requirements and where payload sizes exceed real-time limits.

SageMaker asynchronous inference integrates natively with Amazon SNS to send notifications when inference completes or fails, which directly satisfies the user notification requirement.

Batch transform jobs are intended for offline, scheduled processing of large datasets and do not provide built-in user notification mechanisms. Amazon EFS is not required because SageMaker natively integrates with S3 for inference input and output.

AWS explicitly documents asynchronous inference as the preferred solution for large image inference with completion notifications.

Therefore, Option B is the correct and AWS-verified answer.

This problem describes a recommendation system with millions of records, many categorical variables, and a high-dimensional sparse feature space created by one-hot encoding. AWS documentation explicitly recommends Amazon SageMaker Factorization Machines (FM) for such use cases.

Factorization Machines are designed to handle sparse datasets efficiently and to model interactions between categorical features without explicitly enumerating all feature combinations. This capability makes FM particularly well-suited for recommendation problems such as predicting user-item interactions, including destination recommendations.

With 2,000 possible airport destinations, the target space is large and sparse. One-hot encoding further increases sparsity. Factorization Machines address this challenge by learning latent factors that capture relationships between features, even when many feature combinations are rarely observed.

Option A (CatBoost) is not an Amazon SageMaker built-in algorithm and therefore does not meet the requirement. Option B (DeepAR) is a time-series forecasting algorithm, not intended for recommendation or classification problems. Option D (k-means) is an unsupervised clustering algorithm and cannot directly predict a specific destination label.

AWS documentation explicitly lists recommendation systems and click prediction as primary use cases for the SageMaker Factorization Machines algorithm.

Therefore, Option C is the correct and AWS-verified choice.

Managing permissions for multiple Amazon SageMaker notebook instances can become complex when handled individually. To centralize and streamline permission management, AWS recommends creating a single IAM role with the necessary permissions and attaching this role to each notebook instance used by the data science team.

Steps to Implement the Solution:

Create a Single IAM Role with Necessary Permissions:

Define an IAM role that encompasses all permissions required by the data scientists for their tasks. This includes permissions for SageMaker operations and any other AWS services they interact with.

AWS provides managed policies like AmazonSageMakerFullAccess that can be attached to the role to grant comprehensive SageMaker permissions.(IAM Policies for SageMaker)

Attach the IAM Role to Each Notebook Instance:

When creating or updating a SageMaker notebook instance, specify the IAM role created in the previous step. This ensures that all notebook instances operate under a consistent set of permissions.

In the SageMaker console, during the notebook instance setup, you can choose an existing IAM role to associate with the instance.(Creating SageMaker Workspaces)

Benefits of This Approach:

Centralized Permission Management:By using a single IAM role, you simplify the process of updating permissions. Changes to the role's policies automatically propagate to all associated notebook instances, ensuring consistent access control.

Adherence to Best Practices:AWS recommends using IAM roles to manage permissions for applications running on services like SageMaker. This approach avoids the need to manage individual user permissions separately.(IAM Best Practices for SageMaker)

Alternative Options and Their Drawbacks:

Option B: Creating a single IAM group and adding data scientists to it does not directly associate the group with notebook instances. IAM groups are used to manage user permissions, not to assign roles to AWS resources like notebook instances.

Option C: Using a single IAM user with the AdministratorAccess policy is not recommended due to security risks associated with granting broad permissions and the challenges in managing shared user credentials.

Option D: Associating an IAM group with a role and then with notebook instances is not a valid approach, as IAM groups cannot be directly associated with AWS resources.

Conclusion: Option A is the most effective solution to centralize and manage permissions for SageMaker notebook instances, aligning with AWS best practices for IAM role management.

Amazon Bedrock is a fully managed AWS service and does not reside inside customer VPCs. To allow private connectivity from EC2 instances in a private subnet without using the public internet, AWS documentation recommends AWS PrivateLink.

By creating an interface VPC endpoint for Amazon Bedrock, traffic between the EC2 instances and Bedrock remains entirely within the AWS network. This approach preserves the private subnet configuration and meets strict security requirements.

Security groups alone cannot establish connectivity to a managed service endpoint. Amazon Bedrock cannot be deployed into a customer subnet, making Option C invalid. AWS Direct Connect is designed for on-premises connectivity, not for accessing AWS managed services from within a VPC.

AWS explicitly documents that PrivateLink is the correct mechanism for private access to Amazon Bedrock.

Therefore, Option B is the correct and AWS-verified answer.

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 false positives in this scenario, the ML engineer should prioritize models with high recall to reduce the likelihood of missing positive cases.

AWS recommends lifecycle configuration scripts as the simplest and most direct way to customize Amazon SageMaker Notebook Instances at creation time. Lifecycle configurations run automatically when a notebook instance is created or started, allowing scripts, packages, and system dependencies to be installed without manual intervention.

This approach is fully supported, requires no additional infrastructure, and integrates directly with the notebook creation workflow. The script can be reused across notebooks, ensuring consistency.

Options B, C, and D introduce unnecessary complexity, such as container management, private package repositories, or event-driven orchestration.

Therefore, lifecycle configuration scripts provide the least operational overhead solution.

SageMaker script mode allows you to bring existing custom Python scripts and run them on AWS with minimal changes. SageMaker provides prebuilt containers for ML frameworks like PyTorch, simplifying the migration process. This approach enables the company to leverage their existing Python scripts and domain knowledge while benefiting from the scalability and managed environment of SageMaker. It requires the least effort compared to building custom containers or retraining models from scratch.

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.

AWS Glue DataBrew provides an easy-to-use interface for preparing and transforming data, including masking or obfuscating sensitive information. It offers built-in data masking features, allowing the ML engineer to handle sensitive data securely while retaining its structure and meaning. This solution is efficient and requires minimal coding, making it ideal for ensuring sensitive data is masked before model building begins.