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

The problem described is a patient segmentation use case, which is a classic example of unsupervised learning. The objective is to group patients with similar characteristics without predefined labels. AWS documentation clearly states that Amazon SageMaker k-means is designed specifically for clustering and segmentation tasks.

The SageMaker k-means algorithm groups data points into clusters based on feature similarity and requires the user to define the number of clusters using the k hyperparameter. This directly satisfies the requirement to “determine the number of groups by using hyperparameters.” AWS recommends k-means for applications such as customer segmentation, risk grouping, and pattern discovery in healthcare data.

Option A (XGBoost) is a supervised learning algorithm used for classification and regression. The max_depth hyperparameter controls tree complexity, not the number of groups, making it unsuitable for this task.

Option C (DeepAR) is a time-series forecasting algorithm optimized for predicting future values, not clustering patients.

Option D (Random Cut Forest) is an anomaly detection algorithm. While useful for identifying outliers or unusual patient behavior, it does not perform clustering or group segmentation.

AWS SageMaker documentation explicitly identifies k-means as the correct choice when the goal is to partition data into a predefined number of clusters using a tunable hyperparameter.

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

The text feature contains high-cardinality categorical values with minor spelling variations, which is a common real-world data quality issue. Traditional encoding techniques such as ordinal encoding (Option A) or one-hot encoding (Option C) would treat each misspelled value as a completely separate category, resulting in poor generalization and very high dimensionality.

Similarity encoding addresses this problem by grouping or encoding categories based on string similarity. Categories that differ only slightly—such as spelling errors—are encoded with similar numerical representations. Amazon SageMaker Data Wrangler supports similarity encoding specifically for such noisy categorical features.

Target encoding (Option D) depends on the target label distribution and risks target leakage if not carefully applied.

Therefore, similarity encoding is the most appropriate and AWS-recommended solution.

Amazon Data Firehose supports near real-time delivery by configuring the buffer interval and buffer size. AWS documentation states that setting the buffer interval to the minimum (as low as 1 second) enables low-latency ingestion.

By using zero or minimal buffering and tuning PutRecordBatch, data is delivered to OpenSearch almost immediately, enabling sub-second dashboard updates.

DataSync and SQS are not designed for real-time streaming analytics. Increasing the buffer interval worsens latency.

AWS explicitly recommends Firehose with minimal buffering for real-time OpenSearch ingestion.

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

The Amazon SageMaker Model Registry is designed to manage and catalog ML models, including those hosted in Amazon ECR. By creating a model group for each model in the SageMaker Model Registry and setting up cross-account resource policies, the company can establish a central catalog in a new AWS account. This allows all models from the initial accounts to be accessible in a unified, centralized manner for better organization, management, and governance. This solution leverages existing AWS services and ensures scalability and minimal operational overhead.

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.

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.

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 Macie is a fully managed data security and privacy service that uses machine learning to discover and classify sensitive data in Amazon S3. It is purpose-built to identify sensitive data with minimal operational overhead. After identifying the sensitive data, you can use AWS Lambda functions to automate the process of removing or redacting the sensitive data, ensuring efficiency and integration with the hybrid cloud environment. This solution requires the least development effort and aligns with the requirement to handle sensitive data effectively.

AWS provides Amazon SageMaker Pipelines as the native CI/CD solution for ML. Pipelines can automatically trigger retraining when new data arrives in Amazon S3, handle large datasets (such as 10 GB files), and orchestrate preprocessing, training, evaluation, and deployment steps.

For governance and auditing, Amazon SageMaker Model Registry integrates directly with Pipelines to manage model versions, approval status, and metadata such as training metrics. This combination eliminates the need for custom tracking logic and ensures traceability across the ML lifecycle.

Option A lacks native ML lineage and version governance. Option C is unsuitable for large data and complex workflows. Option D is manual and not scalable.

Therefore, using SageMaker Pipelines with the Model Registry is the correct and AWS-recommended solution.