Amazon MLA-C01 Exam Dumps

The requirement that each parameter trial is built based on the performance of the previous trial is the defining characteristic of Bayesian optimization. In Amazon SageMaker Automatic Model Tuning, Bayesian optimization uses prior trial results to intelligently select the next set of hyperparameters, making it far more efficient than grid or random search---especially in large, mixed search spaces.

This scenario includes both categorical (20) and continuous (7) hyperparameters and allows up to 1,000 training jobs, which is well within the supported limits of SageMaker AMT. Bayesian optimization natively supports mixed parameter types and is explicitly recommended by AWS for large, high-dimensional search spaces where exhaustive grid search is impractical.

Option A and D (grid search) do not meet the requirement because grid search evaluates combinations independently and does not learn from previous trials. Additionally, grid search becomes computationally infeasible as dimensionality increases.

Option B (random search) also evaluates trials independently and does not leverage previous results, violating the core requirement.

Therefore, defining both categorical and continuous parameters and using Bayesian optimization with a maximum of 1,000 jobs is the correct and AWS-recommended solution.

Amazon SageMaker Clarify is the AWS service used to detect and quantify bias and fairness metrics in ML models. When bias monitoring jobs run, Clarify publishes bias metrics directly to Amazon CloudWatch.

CloudWatch metrics can be visualized using CloudWatch dashboards or integrated into other monitoring tools, making them ideal for real-time or periodic bias reporting.

CloudTrail logs API activity and does not capture ML metrics. EventBridge and SNS are used for event routing and notifications, not metric visualization.

AWS documentation explicitly states that Clarify bias metrics are emitted to Amazon CloudWatch, which is the correct source for dashboards.

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

AWS recommends SageMaker Serverless Inference for workloads with intermittent or unpredictable traffic. Serverless inference automatically scales compute resources to zero when idle, eliminating costs during periods with no traffic.

For business-hour traffic spikes, provisioned concurrency ensures low-latency responses while still avoiding the cost of continuously running instances. This model is especially cost-effective for CPU-based inference workloads.

Real-time endpoints incur costs even when idle, and asynchronous inference is designed for long-running jobs rather than low-latency predictions.

AWS documentation explicitly states that Serverless Inference is the most cost-effective option for intermittent real-time workloads.

Therefore, Option B is the correct choice.

Amazon Kendra Experience Builder provides a fully managed, low-code solution for building conversational search and question-answering applications. AWS documentation states that Kendra natively supports semantic search, vector embeddings, and vector-based retrieval, making it well suited for RAG-style applications with minimal development effort.

When integrated with Amazon Bedrock, Kendra can act as the retrieval layer, handling document ingestion, indexing, embedding generation, and relevance ranking automatically. This eliminates the need to manually manage embedding models, vector databases, and search logic.

Options B and C require custom schema design, vector indexing, query logic, and operational management of PostgreSQL instances. Although pgvector supports vector search, it significantly increases development and maintenance effort. Option D is unrelated to vector search and is used only for metadata cataloging.

AWS explicitly positions Amazon Kendra as the fastest way to build enterprise-grade conversational assistants that integrate with foundation models.

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

The correct answer is to use Kinesis Data Streams for ingestion, AWS Lambda for transformation, and S3 for durable storage. Kinesis Data Streams is purpose-built for high-throughput, low-latency streaming ingestion at 500 MB/sec scales and integrates seamlessly with downstream processors. AWS Lambda provides serverless, low-latency transformation capabilities ideal for real-time feature engineering without managing infrastructure. Amazon S3 is the standard durable repository for raw data at scale, supporting monthly re-training workflows cost-effectively.

Why other options are incorrect:

• Option 2: S3 Transfer Acceleration and EFS are not optimized for streaming ingestion patterns; EFS is filesystem-based storage unsuitable for raw data warehousing.
• Option 3: While Kafka and FSx are valid for some use cases, they add operational complexity for this scenario and Kafka on EC2 requires manual management; DataBrew is better suited for batch data cleaning than real-time transformation.
• Option 4: Firehose to Redshift adds unnecessary transformation overhead; DynamoDB is not appropriate for storing raw clickstream data at this scale.
• Option 5: DMS is for database migration, not real-time data ingestion; EBS volumes are inappropriate for distributed data storage.