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Amazon MLA-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.

Topic 2

Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.

Topic 3

ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.

Topic 4

Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI
CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q37-Q42):

NEW QUESTION # 37
An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning.
The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain.
Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)

A. The ML engineer must deploy the model to a SageMaker endpoint.
B. The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.
C. The ML engineer and the Canvas user must be in separate SageMaker domains.
D. The ML engineer must host the model on AWS Marketplace.
E. The model must be registered in the SageMaker Model Registry.

Answer: B,E

Explanation:
The SageMaker Canvas user needs permissions to access the Amazon S3 bucket where the model artifacts are stored to retrieve the model for use in Canvas.
Registering the model in the SageMaker Model Registry allows the model to be tracked and managed within the SageMaker ecosystem. This makes it accessible for tuning and deployment through SageMaker Canvas.
This combination ensures proper access control and integration within SageMaker, enabling the Canvas user to work with the model.

NEW QUESTION # 38
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.
C. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.
D. Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.

Answer: C

Explanation:
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.

NEW QUESTION # 39
An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?

A. Increase the learning rate.
B. Decrease the learning rate.
C. Introduce early stopping.
D. Increase the size of the test set.

Answer: B

Explanation:
In training neural networks using Stochastic Gradient Descent (SGD), the learning rate is a critical hyperparameter that influences the convergence behavior of the model. Observing oscillations in training and validation loss suggests that the learning rate may be too high, causing the optimization process to overshoot minima in the loss landscape.
Understanding the Impact of Learning Rate:
* High Learning Rate:A high learning rate can cause the model parameters to update too aggressively, leading to oscillations or divergence in the loss function. This manifests as the loss decreasing for a few epochs and then increasing, repeating this cycle without stable convergence.
* Low Learning Rate:A low learning rate results in smaller parameter updates, allowing the model to converge more steadily to a minimum, albeit potentially at a slower pace.
Recommended Action:
Decreasing the learning rate allows for more precise adjustments to the model parameters, facilitating smoother convergence and reducing oscillations in the loss function. This adjustment helps the model settle into minima more effectively, improving overall performance.
Supporting Evidence:
Research indicates that large learning rates can lead to phenomena such as "catapults," where spikes in training loss occur due to aggressive updates. Reducing the learning rate mitigates these issues, promoting stable training dynamics.
References:
* Catapults in SGD: Spikes in the Training Loss and Their Impact on Generalization Through Feature Learning
* Lecture 7: Training Neural Networks, Part 2 - Stanford University
Conclusion:
To address oscillating training and validation loss during neural network training with SGD, decreasing the learning rate is an effective strategy. This adjustment facilitates smoother convergence and enhances the model's performance on the test set.

NEW QUESTION # 40
A company has an ML model that needs to run one time each night to predict stock values. The model input is
3 MB of data that is collected during the current day. The model produces the predictions for the next day.
The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?

A. Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.
B. Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.
C. Use an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.
D. Use a multi-model serverless endpoint. Enable caching.

Answer: B

Explanation:
A serverless inference endpoint in Amazon SageMaker is ideal for use cases where the model is invoked infrequently, such as running one time each night. It eliminates the cost of idle resources when the model is not in use. Setting the MaxConcurrency parameter to 1 ensures cost-efficiency while supporting the required single nightly invocation. This solution minimizes costs and matches the requirement to process a small amount of data quickly.

NEW QUESTION # 41
A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.
Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

A. Dedicated Instances
B. Spot Instances
C. Reserved Instances
D. On-Demand Instances

Answer: B

Explanation:
Scenario:The company needs to run a batch job for 90 minutes every weekend over the next 6 months. The processing can handle interruptions, and cost-effectiveness is a priority.
Why Spot Instances?
* Cost-Effective:Spot Instances provide up to 90% savings compared to On-Demand Instances, making them the most cost-effective option for batch processing.
* Interruption Tolerance:Since the processing can tolerate interruptions, Spot Instances are suitable for this workload.
* Batch-Friendly:Spot Instances can be requested for specific durations or automatically re-requested in case of interruptions.
Steps to Implement:
* Create a Spot Instance Request:
* Use the EC2 console or CLI to request Spot Instances with desired instance type and duration.
* Use Auto Scaling:Configure Spot Instances with an Auto Scaling group to handle instance interruptions and ensure job completion.
* Run the Batch Job:Use tools like AWS Batch or custom scripts to manage the processing.
Comparison with Other Options:
* Reserved Instances:Suitable for predictable, continuous workloads, but less cost-effective for a job that runs only once a week.
* On-Demand Instances:More expensive and unnecessary given the tolerance for interruptions.
* Dedicated Instances:Best for isolation and compliance but significantly more costly.
References:
* Amazon EC2 Spot Instances
* Best Practices for Using Spot Instances
* AWS Batch for Spot Instances

NEW QUESTION # 42
......

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