VALID AMAZON MLA-C01 VCE DUMPS | MLA-C01 PDF DOWNLOAD

Valid Amazon MLA-C01 Vce Dumps | MLA-C01 PDF Download

Valid Amazon MLA-C01 Vce Dumps | MLA-C01 PDF Download

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q72-Q77):

NEW QUESTION # 72
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. Increase the size of the test set.
  • C. Decrease the learning rate.
  • D. Introduce early stopping.

Answer: C

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 # 73
A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.
The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.
How should the company deploy the model into production to meet these requirements?

  • A. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster. Use ECS scheduled scaling that is based on the CPU of the ECS cluster.
  • B. Create a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model.
  • C. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Deploy the model in Amazon EKS. Set horizontal pod auto scaling to scale replicas based on the memory metric.
  • D. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences. Use the ALBRequestCountPerTarget metric as the metric for auto scaling.

Answer: B

Explanation:
Amazon SageMaker real-time inference endpoints are designed to provide low-latency predictions in production environments. They offer built-in auto scaling to handle unpredictable bursts of requests, ensuring high availability and responsiveness. This approach is fully managed, reduces operational complexity, and is optimized for the range of request sizes (1 KB to 3 MB) specified in the requirements.


NEW QUESTION # 74
A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ## in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?

  • A. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
  • B. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
  • C. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
  • D. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.

Answer: C

Explanation:
Amazon SageMaker Pipelines is designed for creating, automating, and managing end-to-end ML workflows, including complex data preprocessing tasks. It supports handling large datasets and can integrate with custom steps, such as NLP transformations. By combining SageMaker Pipelines with Amazon EventBridge, the entire workflow can be triggered and automated efficiently, meeting the requirements for scalability, automation, and processing complexity.


NEW QUESTION # 75
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

  • A. Random search
  • B. Grid search
  • C. Hyperbaric!
  • D. Bayesian optimization

Answer: C

Explanation:
Hyperband is a hyperparameter tuning strategy designed to minimize computation time by adaptively allocating resources to promising configurations and terminating underperforming ones early. It efficiently balances exploration and exploitation, making it ideal for large datasets and deep learning models where training can be computationally expensive.


NEW QUESTION # 76
A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance.
Which solution will meet these requirements in the LEAST amount of time?

  • A. Transfer the data to a new S3 bucket that provides S3 Express One Zone storage. Adjust the training job to use the new S3 bucket.
  • B. Create an Amazon Elastic File System (Amazon EFS) file system. Transfer the existing data to the file system. Adjust the training job to read from the file system.
  • C. Create an Amazon FSx for Lustre file system. Link the file system to the existing S3 bucket. Adjust the training job to read from the file system.
  • D. Create an Amazon ElastiCache (Redis OSS) cluster. Link the Redis OSS cluster to the existing S3 bucket. Stream the data from the Redis OSS cluster directly to the training job.

Answer: C

Explanation:
Amazon FSx for Lustre is designed for high-performance workloads like ML training. It provides fast, low- latency access to data by linking directly to the existing S3 bucket and caching frequently accessed files locally. This significantly improves training performance compared to directly accessing millions of files from S3. It requires minimal changes to the training job and avoids the overhead of transferring or restructuring data, making it the fastest and most efficient solution.


NEW QUESTION # 77
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