MLA-C01 Exam Dumps - Amazon Web Services AWS Certified Associate Questions and Answers

For asynchronous inference on large datasets, AWS recommends SageMaker batch transform, which is designed for offline and large-scale inference workloads. Batch transform jobs do not require real-time endpoints and can process large volumes of data efficiently.

For monitoring data quality, AWS provides Amazon SageMaker Model Monitor, which supports scheduled monitoring of data quality, schema drift, and feature distribution drift. Model Monitor can publish metrics to Amazon CloudWatch and trigger alerts when thresholds are breached.

The other options lack native ML-specific monitoring capabilities. CloudTrail audits API activity, not data quality. Glue, Batch, and ECS would require extensive custom monitoring logic.

Therefore, SageMaker batch transform combined with Model Monitor is the correct solution.

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.

Amazon FSx for Lustre is designed for high-performance workloads like ML training. It provides fast, low-latency access to data by linking directly to the existing S3 bucket and caching frequently accessed files locally. This significantly improves training performance compared to directly accessing millions of files from S3. It requires minimal changes to the training job and avoids the overhead of transferring or restructuring data, making it the fastest and most efficient solution.

AWS provides Amazon SageMaker Model Monitor as a native solution for detecting data drift and model quality issues in production ML pipelines. Model Monitor continuously analyzes incoming inference data and compares it with baseline training data to identify schema drift, feature distribution drift, and data quality anomalies.

When drift thresholds are violated, Model Monitor generates CloudWatch metrics and alerts. These alerts can directly trigger an AWS Lambda function, which can then programmatically initiate a SageMaker retraining job or start a SageMaker Pipeline execution. This design is explicitly documented by AWS as the recommended architecture for automated retraining workflows.

Option A is incorrect because AWS Glue is a data integration service and does not provide ML-specific drift detection capabilities.

Option B is incorrect because Apache Flink is designed for stream processing, not ML data drift detection.

Option D is incorrect because Amazon QuickSight anomaly detection is intended for business intelligence metrics, not ML feature drift.

Therefore, using SageMaker Model Monitor with AWS Lambda automation is the correct, AWS-native solution for drift-driven retraining.

The requirement is event-driven automation when new data arrives in Amazon S3, followed by training and model registration. Amazon EventBridge natively supports S3 object creation events and can trigger downstream workflows immediately.

By using EventBridge to start an AWS Step Functions workflow that includes a training step and a SageMaker Model Registry registration step, the pipeline runs automatically as soon as new data is uploaded—well within the 24-hour requirement.

Option A introduces unnecessary polling and delay. Option B is time-based and does not ensure alignment with data readiness. Option C is invalid because S3 Lifecycle rules manage object transitions, not workflow execution.

Therefore, EventBridge-triggered Step Functions is the correct solution.

Explaining how a model makes predictions is the domain of model interpretability and explainability. Amazon SageMaker Clarify is designed specifically to provide explanations for ML predictions using techniques such as SHAP (SHapley Additive exPlanations).

SageMaker Clarify can analyze deployed endpoints to show feature importance, explain individual predictions, and quantify how each input feature contributes to the model’s output. This makes it ideal for communicating model behavior to non-technical stakeholders and meeting transparency requirements.

Model Monitor focuses on data and performance drift, not explanations. A/B testing and shadow endpoints compare performance but do not explain predictions.

Therefore, SageMaker Clarify is the correct solution for explaining model predictions.

AWS recommends Retrieval Augmented Generation (RAG) using Amazon Bedrock knowledge bases as the most cost-effective solution for incorporating frequently updated documents into LLM-powered applications.

A Bedrock knowledge base allows the company to store documents in Amazon S3, index them using vector embeddings, and retrieve relevant context dynamically at inference time. This approach eliminates the need for retraining or fine-tuning the model when documents change.

Training or fine-tuning an LLM is expensive, time-consuming, and unnecessary for frequently changing data. Bedrock guardrails are designed for safety and policy enforcement, not knowledge integration.

AWS documentation explicitly states that knowledge bases are the preferred method for dynamic, updatable enterprise content in chat applications.

Therefore, Option D is the correct and most cost-effective solution.

The requirement is to quickly determine whether the target variable is predictable, with minimal development effort. Amazon SageMaker Autopilot is specifically designed for this purpose.

SageMaker Autopilot automatically handles data preprocessing, feature engineering, algorithm selection, model training, and evaluation. It generates multiple candidate models and provides detailed performance metrics, allowing teams to quickly assess predictability without writing custom code.

Option B requires significant manual development. Option C is incorrect because Amazon Macie is a data security and classification service, not an ML modeling service. Option D is unsuitable because Amazon Bedrock models are not intended for structured tabular prediction tasks.

Therefore, SageMaker Autopilot provides the fastest and least effort solution.

The large gap between training accuracy (99%) and validation accuracy (82%) is a textbook case of overfitting. The model has learned patterns that fit the training data extremely well but do not generalize to unseen data.

AWS ML best practices recommend regularization techniques to address overfitting. Dropout layers randomly deactivate neurons during training, preventing the network from relying too heavily on specific paths. L1 and L2 regularization penalize large weights, reducing model complexity and improving generalization. k-fold cross-validation provides a more robust evaluation by training and validating the model across multiple data splits.

Option A increases complexity, which would worsen overfitting. Option C mixes valid ideas (dimensionality reduction) with unrelated changes (loss function choice) and is less targeted. Option D focuses on data quality but does not directly address model variance.

Therefore, implementing dropout, regularization, and k-fold cross-validation is the correct solution.

Early stopping halts training once the performance on the validation dataset stops improving. This prevents the model from overfitting, which is likely the cause of performance degradation after a certain number of epochs.

Dropout is a regularization technique that randomly deactivates neurons during training, reducing overfitting by forcing the model to generalize better. Increasing dropout can help mitigate the problem of performance degradation due to overfitting.