Free Amazon Web Services MLA-C01 Practice Exam with Questions & Answers

Dynamic data masking allows you to control how sensitive data is presented to users at query time, without modifying or storing transformed versions of the source data. Amazon Redshift supports dynamic data masking, which can be implemented with minimal effort. This solution ensures that the data scientist can access the required information while sensitive data remains protected, meeting the requirements efficiently and with the least implementation effort.

The requirement is to detect irregularities (anomalies) in patient data using an unlabeled dataset, which clearly defines an unsupervised anomaly detection problem. According to AWS documentation, Amazon SageMaker Random Cut Forest (RCF) is purpose-built for detecting anomalies in high-dimensional, continuous datasets such as healthcare metrics and time-series–like records.

RCF works by constructing multiple random decision trees that partition the data. Observations that are isolated closer to the root of these trees are more likely to be anomalies. AWS explicitly recommends RCF for use cases such as fraud detection, system monitoring, and healthcare anomaly detection.

The num_trees hyperparameter controls the number of trees in the forest. Increasing num_trees improves anomaly detection accuracy and stability by averaging anomaly scores across more trees, which is especially important in sensitive domains like healthcare. AWS documentation notes that larger forests provide better generalization and more reliable anomaly scores.

Option A (XGBoost) is a supervised learning algorithm and requires labeled data, making it unsuitable. Option B (k-means) performs clustering but does not explicitly detect anomalies. Option C (DeepAR) is designed for time-series forecasting, not anomaly detection in unlabeled datasets.

Therefore, using Amazon SageMaker Random Cut Forest with a higher num_trees value is the most appropriate, scalable, and AWS-recommended solution.

AWS provides Amazon SageMaker Multi-Model Endpoints to host multiple trained models behind a single inference endpoint. This feature dynamically loads models into memory based on request traffic, significantly reducing endpoint sprawl and operational overhead.

Multi-model endpoints are ideal when an organization has hundreds or thousands of related models, such as geographic or regional models, that are not all invoked simultaneously. AWS documentation highlights this approach as a best practice for cost optimization and simplified deployment management.

Option B (multi-container endpoints) is intended for chaining a small number of models, not hundreds. Option C does not address endpoint consolidation. Option D changes the modeling strategy and is not required.

Therefore, using a single SageMaker multi-model endpoint is the correct solution.

The ETL process has two critical characteristics: it is long-running (6 hours) and written in R. AWS Lambda is unsuitable because it has a maximum execution time of 15 minutes. AWS Glue primarily supports Spark-based ETL and does not natively support custom R-based workloads.

AWS documentation recommends using Amazon SageMaker Processing Jobs for long-running, custom data processing workloads. Processing jobs allow users to run arbitrary code in custom Docker containers, making them ideal for migrating on-premises ETL jobs written in R.

By building a custom Docker image that includes the R runtime and required libraries and storing it in Amazon ECR, the company can run the ETL job at scale on managed infrastructure without rewriting the code.

SageMaker script mode is intended for training, not ETL. Therefore, SageMaker processing jobs with a custom container are the correct solution.

Amazon SageMaker Automated Model Tuning supports several hyperparameter search strategies. Bayesian optimization is explicitly designed to model the relationship between hyperparameters and objective metrics using regression techniques. Based on results from previous training jobs, Bayesian optimization predicts which hyperparameter combinations are most likely to improve model performance and evaluates those next.

AWS documentation highlights Bayesian optimization as the preferred strategy when the hyperparameter search space is small to medium and when training jobs are expensive. Because the algorithm learns from prior runs, it avoids wasting resources on unpromising configurations and converges efficiently.

Grid search exhaustively evaluates all combinations and becomes inefficient even with moderately sized search spaces. Random search does not use information from prior runs and is less efficient. Hyperband focuses on aggressive early stopping and resource allocation, not regression-based sequential selection.

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

Adding resource tagging to the SageMaker user profile enables tracking and monitoring of costs associated with specific SageMaker resources.

AWS Budgets allows setting thresholds and automated alerts for costs and usage, making it the ideal service to notify the ML engineer when compute costs reach a specified limit.

This solution is efficient and integrates seamlessly with SageMaker and AWS cost management tools.

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.

SageMaker Asynchronous Inference is designed for long-running inference workloads and large payloads (up to 1 GB). Requests are queued, processed asynchronously, and results are written to Amazon S3.

Real-time endpoints have payload and timeout limits. EC2 and ECS require infrastructure management, increasing operational overhead.

AWS documentation explicitly recommends asynchronous inference for workloads with large inputs and long execution times.

Therefore, Option C is the correct and most efficient solution.