LATEST AMAZON MLA-C01 LEARNING MATERIALS & MLA-C01 LATEST TEST BOOTCAMP

Latest Amazon MLA-C01 Learning Materials & MLA-C01 Latest Test Bootcamp

Latest Amazon MLA-C01 Learning Materials & MLA-C01 Latest Test Bootcamp

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q46-Q51):

NEW QUESTION # 46
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. Introduce early stopping.
  • B. Increase the learning rate.
  • C. Increase the size of the test set.
  • D. Decrease the learning rate.

Answer: D

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 # 47
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

  • A. Use Amazon Comprehend to analyze sentiments of the chat conversations.
  • B. Use random forests to classify sentiments of the chat conversations.
  • C. Train a Naive Bayes classifier to analyze sentiments of the chat conversations.
  • D. Use Amazon Rekognition to analyze sentiments of the chat conversations.

Answer: A

Explanation:
Amazon Comprehend is a fully managed natural language processing (NLP) service that includes a built-in sentiment analysis feature. It can quickly and efficiently analyze text data to determine whether the sentiment is positive, negative, neutral, or mixed. Using Amazon Comprehend requires minimal setup and provides accurate results without the need to train and deploy custom models, making it the fastest and most efficient solution for this task.


NEW QUESTION # 48
An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.
The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.
Which solution will meet these requirements in the MOST operationally efficient way?

  • A. Store users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.
  • B. Configure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.
  • C. Configure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.
  • D. Use Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.

Answer: D

Explanation:
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.


NEW QUESTION # 49
A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day.
Multiple invocations during the analysis period will require quick responses. The company needs AWS to manage the underlying infrastructure and any auto scaling activities.
Which solution will meet these requirements?

  • A. Use Amazon SageMaker Serverless Inference with provisioned concurrency.
  • B. Configure an Auto Scaling group of Amazon EC2 instances to use scheduled scaling.
  • C. Schedule an Amazon SageMaker batch transform job by using AWS Lambda.
  • D. Run the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster on Amazon EC2 with pod auto scaling.

Answer: A

Explanation:
SageMaker Serverless Inference is ideal for workloads with predictable, intermittent demand. By enabling provisioned concurrency, the model can handle multiple invocations quickly during the high-demand 2-hour period. AWS manages the underlying infrastructure and scaling, ensuring the solution meets performance requirements with minimal operational overhead. This approach is cost-effective since it scales down when not in use.


NEW QUESTION # 50
A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories.
Which solution will meet these requirements?

  • A. Use the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR. Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog. Attach a cross-account resource policy to each model group in the initial AWS accounts.
  • B. Configure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account.
  • C. Create a new AWS account with a new ECR repository as the central catalog. Configure ECR cross- account replication between the initial ECR repositories and the central catalog.
  • D. Use an AWS Glue Data Catalog to store the models. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalog. Configure cross-account access to the Data Catalog.

Answer: A

Explanation:
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.


NEW QUESTION # 51
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