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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q317-Q322):

NEW QUESTION # 317
A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes Which function will produce the desired output?

A. Dropout
B. Smooth L1 loss
C. Softmax
D. Rectified linear units (ReLU)

Answer: C

Explanation:
The softmax function is a function that can transform a vector of arbitrary real values into a vector of real values in the range (0,1) that sum to 1. This means that the softmax function can produce a valid probability distribution over multiple classes. The softmax function is often used as the activation function of the output layer in a neural network, especially for multi-class classification problems. The softmax function can assign higher probabilities to the classes with higher scores, which allows the network to make predictions based on the most likely class. In this case, the Machine Learning Specialist wants to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes of animals. Therefore, the softmax function is the most suitable function to produce the desired output.
Softmax Activation Function for Deep Learning: A Complete Guide
What is Softmax in Machine Learning? - reason.town
machine learning - Why is the softmax function often used as activation ...
Multi-Class Neural Networks: Softmax | Machine Learning | Google for ...

NEW QUESTION # 318
A company wants to predict the classification of documents that are created from an application. New documents are saved to an Amazon S3 bucket every 3 seconds. The company has developed three versions of a machine learning (ML) model within Amazon SageMaker to classify document text. The company wants to deploy these three versions to predict the classification of each document.
Which approach will meet these requirements with the LEAST operational overhead?

A. Deploy each model to its own SageMaker endpoint. Create three AWS Lambda functions. Configure each Lambda function to call a different endpoint and return the results. Configure three S3 event notifications to invoke the Lambda functions when new documents are created.
B. Deploy all the models to a single SageMaker endpoint. Treat each model as a production variant.
Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to call each production variant and return the results of each model.
C. Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to create three SageMaker batch transform jobs, one batch transform job for each model for each document.
D. Deploy each model to its own SageMaker endpoint Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to call each endpoint and return the results of each model.

Answer: B

Explanation:
The approach that will meet the requirements with the least operational overhead is to deploy all the models to a single SageMaker endpoint, treat each model as a production variant, configure an S3 event notification that invokes an AWS Lambda function when new documents are created, and configure the Lambda function to call each production variant and return the results of each model. This approach involves the following steps:
Deploy all the models to a single SageMaker endpoint. Amazon SageMaker is a service that can build, train, and deploy machine learning models. Amazon SageMaker can deploy multiple models to a single endpoint, which is a web service that can serve predictions from the models. Each model can be treated as a production variant, which is a version of the model that runs on one or more instances. Amazon SageMaker can distribute the traffic among the production variants according to the specified weights1.
Treat each model as a production variant. Amazon SageMaker can deploy multiple models to a single endpoint, which is a web service that can serve predictions from the models. Each model can be treated as a production variant, which is a version of the model that runs on one or more instances. Amazon SageMaker can distribute the traffic among the production variants according to the specified weights1.
Configure an S3 event notification that invokes an AWS Lambda function when new documents are created.
Amazon S3 is a service that can store and retrieve any amount of data. Amazon S3 can send event notifications when certain actions occur on the objects in a bucket, such as object creation, deletion, or modification. Amazon S3 can invoke an AWS Lambda function as a destination for the event notifications. AWS Lambda is a service that can run code without provisioning or managing servers2.
Configure the Lambda function to call each production variant and return the results of each model. AWS Lambda can execute the code that can call the SageMaker endpoint and specify the production variant to invoke. AWS Lambda can use the AWS SDK or the SageMaker Runtime API to send requests to the endpoint and receive the predictions from the models. AWS Lambda can return the results of each model as a response to the event notification3.
The other options are not suitable because:
Option A: Configuring an S3 event notification that invokes an AWS Lambda function when new documents are created, configuring the Lambda function to create three SageMaker batch transform jobs, one batch transform job for each model for each document, will incur more operational overhead than using a single SageMaker endpoint. Amazon SageMaker batch transform is a service that can process large datasets in batches and store the predictions in Amazon S3. Amazon SageMaker batch transform is not suitable for real- time inference, as it introduces a delay between the request and the response. Moreover, creating three batch transform jobs for each document will increase the complexity and cost of the solution4.
Option C: Deploying each model to its own SageMaker endpoint, configuring an S3 event notification that invokes an AWS Lambda function when new documents are created, configuring the Lambda function to call each endpoint and return the results of each model, will incur more operational overhead than using a single SageMaker endpoint. Deploying each model to its own endpoint will increase the number of resources and endpoints to manage and monitor. Moreover, calling each endpoint separately will increase the latency and network traffic of the solution5.
Option D: Deploying each model to its own SageMaker endpoint, creating three AWS Lambda functions, configuring each Lambda function to call a different endpoint and return the results, configuring three S3 event notifications to invoke the Lambda functions when new documents are created, will incur more operational overhead than using a single SageMaker endpoint and a single Lambda function. Deploying each model to its own endpoint will increase the number of resources and endpoints to manage and monitor.
Creating three Lambda functions will increase the complexity and cost of the solution. Configuring three S3 event notifications will increase the number of triggers and destinations to manage and monitor6.
1: Deploying Multiple Models to a Single Endpoint - Amazon SageMaker
2: Configuring Amazon S3 Event Notifications - Amazon Simple Storage Service
3: Invoke an Endpoint - Amazon SageMaker
4: Get Inferences for an Entire Dataset with Batch Transform - Amazon SageMaker
5: Deploy a Model - Amazon SageMaker
6: AWS Lambda

NEW QUESTION # 319
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents?

A. Create an appropriate pronunciation lexicon.
B. Convert current documents to SSML with pronunciation tags
C. Use Amazon Lex to preprocess the text files for pronunciation
D. Output speech marks to guide in pronunciation

Answer: A

Explanation:
Explanation
A pronunciation lexicon is a file that defines how words or phrases should be pronounced by Amazon Polly. A lexicon can help customize the speech output for words that are uncommon, foreign, or have multiple pronunciations. A lexicon must conform to the Pronunciation Lexicon Specification (PLS) standard and can be stored in an AWS region using the Amazon Polly API. To use a lexicon for synthesizing speech, the lexicon name must be specified in the <speak> SSML tag. For example, the following lexicon defines how to pronounce the acronym W3C:
<lexicon
version="1.0" xmlns="http://www.w3.org/2005/01/pronunciation-lexicon" alphabet="ipa" xml:lang="en-US"
> <lexeme> <grapheme>W3C</grapheme> <alias>World Wide Web Consortium</alias> </lexeme>
</lexicon>
To use this lexicon, the text input must include the following SSML tag:
<speak
version="1.1" xmlns="http://www.w3.org/2001/10/synthesis" xml:lang="en-US"
> <voice name="Joanna"> <lexicon name="w3c_lexicon"/> The <say-as
interpret-as="characters">W3C</say-as> is an international community that develops open standards to ensure the long-term growth of the Web. </voice> </speak> References:
Customize pronunciation using lexicons in Amazon Polly: A blog post that explains how to use lexicons for creating custom pronunciations.
Managing Lexicons: A documentation page that describes how to store and retrieve lexicons using the Amazon Polly API.

NEW QUESTION # 320
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

A. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
B. Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
C. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.
D. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.

Answer: D

Explanation:
To log Amazon SageMaker API calls, the team can use AWS CloudTrail, which is a service that provides a record of actions taken by a user, role, or an AWS service in SageMaker1. CloudTrail captures all API calls for SageMaker, with the exception of InvokeEndpoint and InvokeEndpointAsync, as events1. The calls captured include calls from the SageMaker console and code calls to the SageMaker API operations1. The team can create a trail to enable continuous delivery of CloudTrail events to an Amazon S3 bucket, and configure other AWS services to further analyze and act upon the event data collected in CloudTrail logs1. The auditors can view the CloudTrail log activity report in the CloudTrail console or download the log files from the S3 bucket1.
To receive a notification when the model is overfitting, the team can add code to push a custom metric to Amazon CloudWatch, which is a service that provides monitoring and observability for AWS resources and applications2. The team can use the MXNet metric API to define and compute the custom metric, such as the validation accuracy or the validation loss, and use the boto3 CloudWatch client to put the metric data to CloudWatch3 . The team can then create an alarm in CloudWatch with Amazon SNS to receive a notification when the custom metric crosses a threshold that indicates overfitting . For example, the team can set the alarm to trigger when the validation loss increases for a certain number of consecutive periods, which means the model is learning the noise in the training data and not generalizing well to the validation data.
References:
1: Log Amazon SageMaker API Calls with AWS CloudTrail - Amazon SageMaker
2: What Is Amazon CloudWatch? - Amazon CloudWatch
3: Metric API - Apache MXNet documentation
4: CloudWatch - Boto 3 Docs 1.20.21 documentation
5: Creating Amazon CloudWatch Alarms - Amazon CloudWatch
6: What is Amazon Simple Notification Service? - Amazon Simple Notification Service
7: Overfitting and Underfitting - Machine Learning Crash Course

NEW QUESTION # 321
An Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

A. The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1
B. The model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model
C. The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1
D. The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model

Answer: D

NEW QUESTION # 322
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MLS-C01 Formal Test & MLS-C01 Reliable Exam Cost

Free PDF Quiz 2025 Useful Amazon MLS-C01: AWS Certified Machine Learning - Specialty Formal Test

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q317-Q322):

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