Pass the Amazon Web Services MLA-C01 Questions and answers with ValidTests

The core requirement is to identify and remove sensitive data from content stored in Amazon S3 with minimal operational overhead. Amazon Macie is purpose-built to automatically discover, classify, and protect sensitive data (such as PII) in Amazon S3 using machine learning. Macie continuously scans S3 objects and produces findings that identify sensitive data types and locations without requiring custom ML pipelines or infrastructure.

Once Macie identifies sensitive data, AWS Lambda can be used to automate remediation—such as redacting, masking, or deleting sensitive fields—based on Macie findings. This event-driven approach is serverless, scales automatically, and minimizes operations.

Option A increases overhead by moving the model and building custom detection logic. Option B introduces unnecessary compute orchestration (ECS/Fargate and Batch). Option D uses Amazon Comprehend for entity detection, which is not optimized for broad S3 data discovery and requires EC2 management.

Therefore, using Amazon Macie for detection and Lambda for remediation is the most efficient and AWS-recommended solution.

Amazon EC2 Spot Instances provide unused compute capacity at discounts of up to 90%. AWS documentation strongly recommends Spot Instances for interruptible workloads such as long-running ML training jobs that can resume from checkpoints.

By enabling automatic checkpointing, SageMaker saves model state periodically to Amazon S3. If the Spot Instance is interrupted, training can resume with minimal loss of progress.

Reserved Instances and Savings Plans require long-term commitment and are not as cost-effective for sporadic workloads. On-Demand Instances are the most expensive option.

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

Amazon Q Business provides built-in guardrails to control and constrain model responses. One such control is the ability to define blocked phrases, which explicitly prevents specified terms from appearing in generated responses.

Configuring the competitor’s name as a blocked phrase guarantees that Amazon Q Business will never include that name in responses, fully meeting the requirement. This approach is deterministic and does not rely on ranking or retrieval behavior.

Option B is incorrect because retrievers control what documents are fetched, not how responses are generated. Option C adds unnecessary complexity and does not guarantee exclusion in generated answers. Option D only deprioritizes content and does not prevent it from appearing.

Therefore, blocking the competitor’s name is the correct solution.

This scenario clearly describes an overfitting problem. The neural network performs well on training data but fails to generalize to evaluation data. According to AWS Machine Learning and deep learning best practices, overfitting occurs when a model learns noise or overly specific patterns in the training data instead of generalizable relationships.

L1 and L2 regularization are well-established techniques to combat overfitting. They work by penalizing large weight values during optimization, effectively constraining the model’s complexity. L1 regularization encourages sparsity, while L2 regularization (weight decay) smooths the weight distribution. AWS documentation recommends regularization as a primary method for improving generalization when a model shows a large gap between training and evaluation performance.

Option B is incorrect because removing dropout layers would increase overfitting, not reduce it. Dropout is itself a regularization technique.

Option C would likely worsen overfitting, as training for more epochs allows the model to memorize the training data even further.

Option D increases model complexity, which again exacerbates overfitting rather than resolving it.

Therefore, penalizing large weights using L1 or L2 regularization is the correct solution.

SageMaker Pipelines provides a directed acyclic graph (DAG) view for managing and visualizing ML workflows with fine-grained control. It integrates seamlessly with SageMaker Studio, offering an intuitive interface for workflow orchestration.

SageMaker ML Lineage Tracking keeps a running history of experiments and tracks the lineage of datasets, models, and training jobs. This feature supports model governance, auditing, and compliance verification requirements.

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.

AWS Glue DataBrew is specifically designed to provide no-code and low-code data preparation for analytics and machine learning. It supports common file formats such as Apache Parquet and integrates directly with Amazon S3.

Using DataBrew, users can visually create recipes that apply transformations such as one-hot encoding without writing any code. Once the recipe is defined, a DataBrew job can be run to process the dataset and store the transformed output back into Amazon S3.

Options B, C, and D all require writing SQL or code, which violates the no-code requirement. AWS documentation clearly identifies DataBrew as the correct service for interactive, visual data transformation at scale.

Therefore, Option A is the correct solution.

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.

Text representation of basic units of data processed by LLMs: Token

High-dimensional vectors that contain the semantic meaning of text: Embedding

Enrichment of information from additional data sources to improve a generated response: Retrieval Augmented Generation (RAG)

Comprehensive Detailed Explanation

Token:

Description: A token represents the smallest unit of text (e.g., a word or part of a word) that an LLM processes. For example, "running" might be split into two tokens: "run" and "ing."

Why? Tokens are the fundamental building blocks for LLM input and output processing, ensuring that the model can understand and generate text efficiently.

Embedding:

Description: High-dimensional vectors that encode the semantic meaning of text. These vectors are representations of words, sentences, or even paragraphs in a way that reflects their relationships and meaning.

Why? Embeddings are essential for enabling similarity search, clustering, or any task requiring semantic understanding. They allow the model to "understand" text contextually.

Retrieval Augmented Generation (RAG):

Description: A technique where information is enriched or retrieved from external data sources (e.g., knowledge bases or document stores) to improve the accuracy and relevance of a model's generated responses.

Why? RAG enhances the generative capabilities of LLMs by grounding their responses in factual and up-to-date information, reducing hallucinations in generated text.

By matching these terms to their respective descriptions, the ML engineer can effectively leverage these concepts to build robust and contextually aware generative AI applications on Amazon Bedrock.

Amazon Q Business allows configuring blocked phrases to exclude specific terms or phrases from the responses. By adding the competitor's name as a blocked phrase, the company can ensure that it will not appear in the API responses, meeting the requirement efficiently with minimal configuration.