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The AWS-Certified-Machine-Learning-Specialty Certification Exam consists of multiple-choice and multiple-response questions. AWS-Certified-Machine-Learning-Specialty exam covers a wide range of topics related to machine learning, including data preparation, feature engineering, model selection, training and tuning models, and deploying models. Candidates who pass the exam earn the AWS Certified Machine Learning - Specialty certification, which demonstrates their expertise in using AWS services to develop and deploy machine learning solutions. AWS Certified Machine Learning - Specialty certification is highly valued by employers and can help professionals advance their careers in the field of machine learning.

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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) certification exam is designed for professionals who want to demonstrate their expertise in machine learning on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification exam validates the candidate's ability to design, implement, deploy, and maintain machine learning solutions using AWS services.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q220-Q225):

NEW QUESTION # 220
A data science team is planning to build a natural language processing (NLP) application. The application's text preprocessing stage will include part-of-speech tagging and key phase extraction. The preprocessed text will be input to a custom classification algorithm that the data science team has already written and trained using Apache MXNet.
Which solution can the team build MOST quickly to meet these requirements?

A. Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.
B. Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use Amazon SageMaker built-in Latent Dirichlet Allocation (LDA) algorithm to build the custom classifier.
C. Use an NLP library in Amazon SageMaker for the part-of-speech tagging. Use Amazon Comprehend for the key phase extraction. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.
D. Use Amazon Comprehend for the part-of-speech tagging, key phase extraction, and classification tasks.

Answer: A

Explanation:
Amazon Comprehend is a natural language processing (NLP) service that can perform part-of-speech tagging and key phrase extraction tasks. AWS Deep Learning Containers are Docker images that are pre-installed with popular deep learning frameworks such as Apache MXNet. Amazon SageMaker is a fully managed service that can help build, train, and deploy machine learning models. Using Amazon Comprehend for the text preprocessing tasks and AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier is the solution that can be built most quickly to meet the requirements.
References:
Amazon Comprehend
AWS Deep Learning Containers
Amazon SageMaker

NEW QUESTION # 221
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?

A. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.
B. Organize the Docker container's file structure to execute on GPU instances.
C. Build the Docker container to be NVIDIA-Docker compatible.
D. Bundle the NVIDIA drivers with the Docker image.

Answer: D

NEW QUESTION # 222
An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images Which of the following should be used to resolve this issue? (Select TWO)

A. Make the neural network architecture complex.
B. Add vanishing gradient to the model
C. Add L2 regularization to the model
D. Use gradient checking in the model
E. Perform data augmentation on the training data

Answer: B,E

NEW QUESTION # 223
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:

Based on the model evaluation results, why is this a viable model for production?

A. The precision of the model is 86%, which is less than the accuracy of the model.
B. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
C. The precision of the model is 86%, which is greater than the accuracy of the model.
D. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.

Answer: D

NEW QUESTION # 224
A data scientist uses Amazon SageMaker Data Wrangler to define and perform transformations and feature engineering on historical data. The data scientist saves the transformations to SageMaker Feature Store.
The historical data is periodically uploaded to an Amazon S3 bucket. The data scientist needs to transform the new historic data and add it to the online feature store The data scientist needs to prepare the .....historic data for training and inference by using native integrations.
Which solution will meet these requirements with the LEAST development effort?

A. Configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket.
B. Use Apache Airflow to orchestrate a set of predefined transformations on each new dataset that arrives in the S3 bucket.
C. Use AWS Lambda to run a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket.
D. Run an AWS Step Functions step and a predefined SageMaker pipeline to perform the transformations on each new dalaset that arrives in the S3 bucket

Answer: A

Explanation:
The best solution is to configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket. This solution requires the least development effort because it leverages the native integration between EventBridge and SageMaker Pipelines, which allows you to trigger a pipeline execution based on an event rule. EventBridge can monitor the S3 bucket for new data uploads and invoke the pipeline that contains the same transformations and feature engineering steps that were defined in SageMaker Data Wrangler. The pipeline can then ingest the transformed data into the online feature store for training and inference.
The other solutions are less optimal because they require more development effort and additional services.
Using AWS Lambda or AWS Step Functions would require writing custom code to invoke the SageMaker pipeline and handle any errors or retries. Using Apache Airflow would require setting up and maintaining an Airflow server and DAGs, as well as integrating with the SageMaker API.
References:
* Amazon EventBridge and Amazon SageMaker Pipelines integration
* Create a pipeline using a JSON specification
* Ingest data into a feature group

NEW QUESTION # 225
......

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