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

Amazon Comprehend provides pre-built functionality for key phrase extraction and can identify meaningful keywords from documents with minimal setup or operational overhead. It eliminates the need for manual preprocessing, stemming, or stop-word removal and does not require custom model development or infrastructure management. This makes it the most efficient and low-maintenance solution for the task.

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.

The primary goal is to produce the most complete dataset while handling missing values and extreme outliers appropriately. Dropping samples (Option A) or columns (Option D) would reduce data completeness and potentially remove valuable information, which contradicts the requirement.

Imputation is therefore the correct approach. Between mean and median imputation, AWS ML best practices recommend using the median when features contain outliers. The mean is sensitive to extreme values and can be skewed significantly, leading to imputed values that are not representative of the typical data distribution. In contrast, the median is robust to outliers, making it a better statistical estimator for central tendency in such datasets.

Amazon SageMaker Data Wrangler supports median imputation as a built-in transformation, enabling ML engineers to handle missing values consistently across large tabular datasets without custom code. This approach preserves all rows and columns while minimizing distortion caused by extreme values, which is particularly important for neural networks that are sensitive to input distributions.

Therefore, imputing missing values with the median value provides the most complete and statistically appropriate dataset for training.

Monitoring bias drift in deployed machine learning models is crucial to ensure fairness and accuracy over time. Amazon SageMaker Clarify provides tools to detect bias in ML models, both during training and after deployment. To monitor bias drift for models deployed to real-time endpoints, an effective approach involves orchestrating SageMaker Clarify jobs using AWS Lambda functions.

Implementation Steps:

Set Up Data Capture:

Enable data capture on the SageMaker endpoint to record input data and model predictions. This captured data serves as the basis for bias analysis.

Develop a Lambda Function:

Create an AWS Lambda function configured to initiate a SageMaker Clarify job. This function will process the captured data to assess bias metrics.

Schedule or Trigger the Lambda Function:

Configure the Lambda function to run on-demand or at scheduled intervals using Amazon CloudWatch Events or EventBridge. This setup allows for regular bias monitoring as per the application's requirements.

Analyze and Respond to Results:

After each Clarify job completes, review the generated bias reports. If bias drift is detected, take appropriate actions, such as retraining the model or adjusting data preprocessing steps.

Advantages of This Approach:

Automation: Utilizing AWS Lambda for orchestrating Clarify jobs enables automated and scalable bias monitoring without manual intervention.

Cost-Effectiveness: AWS Lambda's serverless nature ensures that you only pay for the compute time consumed during the execution of the function, optimizing resource usage.

Flexibility: The solution can be tailored to specific monitoring needs, allowing for adjustments in monitoring frequency and analysis parameters.

By implementing this solution, the company can effectively monitor bias drift in real-time, ensuring that the AI application maintains fairness and accuracy throughout its lifecycle.

AWS enterprise ML best practices recommend using Amazon SageMaker Model Registry to manage models throughout their lifecycle. The Model Registry is designed specifically to catalog models, track versions, and associate metadata such as training metrics, approval status, and deployment history.

Model Registry introduces the concept of model groups, which act as logical containers for different versions of the same model. Each model version within a group automatically inherits versioning, metadata tracking, and governance controls. This eliminates the operational burden of manually managing model versions and ensures consistent lineage and traceability across development, testing, and production environments.

Option A is less optimal because manually tagging model versions increases operational complexity and does not take full advantage of the built-in version management features provided by model groups.

Options C and D are incorrect because Amazon ECR is a container image repository, not a model governance or lifecycle management service. Using ECR to manage ML model versions would require custom tooling and manual metadata handling, significantly increasing operational overhead.

By using model groups within SageMaker Model Registry, the company gains a centralized, scalable, and AWS-native solution for enterprise AI governance. This approach directly aligns with AWS documentation for managing model catalogs, version control, and metadata association while minimizing manual intervention.

Amazon FSx for NetApp ONTAP allows mounting the file system as a network-attached storage (NAS) volume. Since the FSx for ONTAP file system and SageMaker instance are in the same VPC, you can directly mount the file system to the SageMaker instance. This approach ensures efficient access to the 6 TB of training data without the need to duplicate or transfer the data, meeting the requirements with minimal complexity and operational overhead.

For cross-account Amazon S3 access, AWS requires two components:

An S3 bucket policy in the owning account (Account A) that grants access to a principal in another account

An IAM role policy in the consuming account (Account B) that allows the service to access the bucket

Amazon SageMaker training jobs assume an IAM role in the account where the job runs—in this case, Account B. Therefore, the IAM policy must be attached to the SageMaker execution role in Account B.

The S3 bucket policy must reside in Account A because bucket policies are owned and enforced by the bucket owner. This policy explicitly allows the IAM role from Account B to read the training data.

Any other combination fails either because the policy is in the wrong account or because the role is not the one used by SageMaker.

AWS documentation clearly describes this pattern as the correct way to grant cross-account access for SageMaker training jobs.

Therefore, Option B is the correct and AWS-aligned solution.

Amazon SageMaker Pipelines provides native orchestration of ML workflows with fine-grained control, DAG-based visualization, and seamless integration with SageMaker Studio. AWS documentation explicitly states that Pipelines is designed for end-to-end ML workflow automation and visualization.

SageMaker ML Lineage Tracking records relationships between datasets, models, training jobs, and endpoints, enabling full auditability and governance, which is essential for compliance.

SageMaker Experiments tracks experiment metrics but does not provide lineage-level governance. CodePipeline is a general CI/CD service and lacks ML-specific DAG visualization and lineage tracking.

AWS best practices recommend combining SageMaker Pipelines + SageMaker Studio + ML Lineage Tracking for enterprise-grade ML workflow management.

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

This solution is the most efficient and involves the least operational overhead:

Amazon Kinesis data streams efficiently handle real-time ingestion of high-volume streaming data.

Amazon Managed Service for Apache Flink provides a fully managed environment for stream processing with built-in support for RANDOM_CUT_FOREST, an algorithm designed for anomaly detection in real-time streaming data.

This approach eliminates the need for deploying and managing additional infrastructure like SageMaker endpoints, Lambda functions, or external tools, making it the most scalable and operationally simple solution.

For Amazon EMR, the primary node and core nodes handle the critical functions of the cluster, including data storage (HDFS) and processing. Running them on On-Demand Instances ensures high availability and prevents data loss, as Spot Instances can be interrupted. The task nodes, which handle additional processing but do not store data, can use Spot Instances to reduce costs without compromising the cluster's resilience or data integrity. This configuration balances cost-effectiveness and reliability.