Amazon Web Services MLA-C01 - AWS Certified Machine Learning Engineer - Associate

Step 1: Update the existing endpoint with a shadow variant. Pick a suitable duration. Start the shadow test.

Step 2: Create a shadow test.

Step 3: Use the SageMaker shadow testing dashboard to analyze the performance differences between the variants.

This is the correct order because SageMaker shadow testing can run against an existing production endpoint. The production variant continues to receive and respond to inference requests, while the shadow variant receives replicated real-time requests but does not return responses to users. That satisfies the requirement to analyze the new model with real inference traffic without affecting the production endpoint. AWS also states that the SageMaker shadow testing dashboard provides side-by-side comparison metrics for the production and shadow variants, such as latency and error rate.

Do not choose “Create a new endpoint that includes the production model and the shadow model,” because the question explicitly says the analysis must not affect the existing production endpoint. Also, do not use the Amazon SageMaker Model Monitoring dashboard here; the specific feature for comparing variants in this scenario is the SageMaker shadow testing dashboard.

Amazon Transcribe is well-suited for converting audio data into text, including Spanish.

Amazon Translate can efficiently translate Spanish text into English if needed.

Amazon Bedrock, with the Jurassic model, is designed for tasks like text summarization and can handle large language models (LLMs) seamlessly. This combination provides a low-code, managed solution to process audio, video, and text data with minimal time and effort.

AWS recommends Amazon SageMaker Model Monitor for detecting data drift and model drift in deployed models. Model Monitor works by comparing live inference data against a baseline, which must first be created from the training dataset.

AWS documentation clearly specifies the required order:

Create a baseline using training data statistics

Create a monitoring schedule to compare incoming data against the baseline

Option A reverses this order and is therefore incorrect. Option C is incorrect because SageMaker Clarify focuses on bias and explainability, not ongoing drift detection. Option D is reactive and does not provide continuous monitoring.

Model Monitor integrates with Amazon CloudWatch, enabling automated alerts and downstream retraining workflows. This proactive approach allows companies to detect degradation early and maintain model quality.

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

The correct answer is C. Use SageMaker Model Monitor to detect data drift. Use an AWS Lambda function to automate the re-training job.

Amazon SageMaker Model Monitor is the AWS-recommended solution for automatically detecting data drift in ML pipelines. Data drift occurs when the statistical properties of input features change over time, potentially reducing model accuracy. Model Monitor continuously analyzes incoming data and compares it to the baseline training dataset to identify deviations. It can track both feature distributions and prediction quality metrics.

Once Model Monitor detects data drift, it can trigger automated workflows using AWS Lambda or Amazon EventBridge. An AWS Lambda function can initiate a SageMaker training job to re-train the model using updated datasets, ensuring the ML model remains accurate and reliable. This setup fully automates the response to drift events, meeting the requirement of automatically initiating re-training.

Option A (AWS Glue) is designed for ETL processes but does not natively detect ML-specific data drift. Option B (Amazon Managed Service for Apache Flink) can process streaming data but does not provide native drift detection for ML pipelines. Option D (Amazon QuickSight anomaly detection) focuses on business intelligence and visual anomaly detection, not automated re-training workflows for ML models.

By integrating SageMaker Model Monitor with Lambda, the ML engineer can maintain model performance proactively, implement automated re-training, and align with AWS best practices for ML solution monitoring, maintenance, and security. This approach ensures continuous validation of model inputs and outputs, reduces operational overhead, and prevents degradation in production ML performance due to unseen data changes.

AWS Glue DataBrew supports datasets sourced directly from Amazon S3 paths. To clean and normalize data, AWS documentation specifies using DataBrew recipes, which define transformation steps such as standardization, deduplication, and formatting.

A profile job is used only for data analysis and statistics generation, not for transformation. A recipe job applies actual transformations to the data and writes the output to Amazon S3.

JDBC connections are used for relational databases, not S3 buckets. Therefore, options C and D are invalid.

AWS clearly documents that the correct workflow is:

Create a DataBrew dataset from an S3 path

Define transformations using a recipe

Run a recipe job to produce cleaned data

Thus, Option B is the correct and AWS-aligned answer.

To generate images, the application must invoke a foundation model hosted on Amazon Bedrock. Invoking the Amazon Titan Image Generator requires the bedrock:InvokeModel permission. Read-only permissions such as bedrock:Get* do not allow inference execution and are insufficient.

The application also interacts with Amazon Simple Queue Service. To process messages, the application must be able to receive messages from the queue and delete them after successful processing. These actions require sqs:ReceiveMessage and sqs:DeleteMessage.

sqs:GetQueueAttributes alone does not allow message consumption. SageMaker permissions are unrelated because Bedrock is a separate service.

Therefore, permissions to invoke the model and receive/delete SQS messages are required.

AWS Lake Formation provides fine-grained access control and simplifies data governance for data lakes. By configuring Lake Formation tags to map ML engineers to their specific campaigns, you can restrict access to both structured and unstructured data in the data lake. This method is operationally efficient, as it centralizes access control management within Lake Formation and ensures consistency across Amazon Athena and S3 bucket access without requiring manual updates to policies or DynamoDB-based custom logic.

The requirement is to quickly determine whether the target variable is predictable, with minimal development effort. Amazon SageMaker Autopilot is specifically designed for this purpose.

SageMaker Autopilot automatically handles data preprocessing, feature engineering, algorithm selection, model training, and evaluation. It generates multiple candidate models and provides detailed performance metrics, allowing teams to quickly assess predictability without writing custom code.

Option B requires significant manual development. Option C is incorrect because Amazon Macie is a data security and classification service, not an ML modeling service. Option D is unsuitable because Amazon Bedrock models are not intended for structured tabular prediction tasks.

Therefore, SageMaker Autopilot provides the fastest and least effort solution.

1️⃣ Simultaneous calls to the endpoint must reach models with the same configuration

Correct strategy: All at once traffic shifting

Why:

“All at once” replaces the old model with the new model immediately. After the switch, all concurrent requests hit only the new model configuration, guaranteeing configuration consistency across simultaneous calls.

2️⃣ The new model must receive only a fraction of the requests for validation before receiving all the traffic

Correct strategy: Canary traffic shifting

Why:

Canary deployments route a small percentage of traffic (for example, 5% or 10%) to the new model first. This allows validation of correctness and performance before shifting 100% of traffic.

3️⃣ Traffic to the new model must increase gradually to ensure that pipelines that rely on the endpoint do not fail because of changes in latency

Correct strategy: Linear traffic shifting guardrail

Why:

Linear traffic shifting gradually increases traffic in equal increments over time and includes guardrails (such as CloudWatch alarms) to automatically roll back if latency or errors exceed thresholds.

AWS Glue DataBrew is designed specifically for no-code and low-code data preparation, making it the fastest and lowest-overhead solution for resolving common data quality issues. DataBrew provides built-in transformations for deduplication, missing value imputation, and outlier handling while preserving data integrity.

Option A uses standard statistical techniques such as median imputation and value normalization, which are widely accepted and maintain the distribution of the data. DataBrew jobs are fully managed and do not require infrastructure setup or maintenance.

Option B deletes records, which can lead to data loss and does not preserve integrity. Option C introduces unnecessary infrastructure complexity and uses poor data imputation practices. Option D provides advanced capabilities but requires more configuration and ML expertise, increasing operational overhead.

AWS documentation clearly positions DataBrew as the preferred solution for quick, reliable data cleaning with minimal effort.

Therefore, Option A is the correct answer.