Peter Wilson Peter Wilson's Profile Page

Peter Wilson Peter Wilson

0 Course Enrolled • 0 Course Completed

Biography

New Amazon MLS-C01 Exam Fee | MLS-C01 Valid Exam Cram

P.S. Free & New MLS-C01 dumps are available on Google Drive shared by ITexamReview: https://drive.google.com/open?id=1W6mJV0zRq0tCUFoEQ5wnheMNPb4ZhKSY

The MLS-C01 exam requires a lot of preparation, hard work, and practice to be successful. To pass the AWS Certified Machine Learning - Specialty (MLS-C01) test, you need to get updated Amazon MLS-C01 dumps. These MLS-C01 questions are necessary to study for the test and pass it on the first try. Updated MLS-C01 Practice Questions are essential prepare successfully for the AWS Certified Machine Learning - Specialty certification exam. But gaining access to updated MLS-C01 questions is challenging for the candidates.

The pass rate is 98.65%, and we pass guarantee and money back guarantee if you fail to pass the exam by using MLS-C01 learning materials of us. We have a broad market in the world with the high quality of MLS-C01 exam dumps, and if you choose us we will help you pass the exam just one time. In addition MLS-C01 Training Materials of us also have free update for one year after purchasing. We also have the professional service stuff to answer all questions of you. If you have a try, you will never regret.

>> New Amazon MLS-C01 Exam Fee <<

Amazon MLS-C01 Valid Exam Cram - Valid MLS-C01 Test Blueprint

Candidates who become Amazon MLS-C01 certified demonstrate their worth in the Amazon field. MLS-C01 certification is proof of their competence and skills. This is a highly sought after credential and it makes career advancement easier for the candidate. To become Amazon MLS-C01 Certified, you must pass the AWS Certified Machine Learning - Specialty (MLS-C01) Exam. For this task, you need actual and updated MLS-C01 Questions.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification program offered by Amazon Web Services (AWS) for individuals who want to validate their skills and knowledge in the field of machine learning. AWS Certified Machine Learning - Specialty certification program is designed to test the candidate's ability to design, implement, deploy, and maintain machine learning solutions on AWS. Candidates who successfully Pass MLS-C01 Exam will earn the AWS Certified Machine Learning - Specialty designation.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q174-Q179):

NEW QUESTION # 174
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

A. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with theregistry hostname and upload it to Amazon S3.
B. Serialize the trained model so the format is compressed for deployment. Build the image and upload it toDocker Hub.
C. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
D. Build the Docker image with the inference code. Tag the Docker image with the registry hostname andupload it to Amazon ECR.

Answer: D

Explanation:
To deploy a model that was trained locally to Amazon SageMaker, the steps are:
* Build the Docker image with the inference code. The inference code should include the model loading, data preprocessing, prediction, and postprocessing logic. The Docker image should also include the dependencies and libraries required by the inference code and the model.
* Tag the Docker image with the registry hostname and upload it to Amazon ECR. Amazon ECR is a fully managed container registry that makes it easy to store, manage, and deploy container images. The registry hostname is the Amazon ECR registry URI for your account and Region. You can use the AWS CLI or the Amazon ECR console to tag and push the Docker image to Amazon ECR.
* Create a SageMaker model entity that points to the Docker image in Amazon ECR and the model artifacts in Amazon S3. The model entity is a logical representation of the model that contains the information needed to deploy the model for inference. The model artifacts are the files generated by the model training process, such as the model parameters and weights. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the model entity.
* Create an endpoint configuration that specifies the instance type and number of instances to use for hosting the model. The endpoint configuration also defines the production variants, which are the different versions of the model that you want to deploy. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the endpoint configuration.
* Create an endpoint that uses the endpoint configuration to deploy the model. The endpoint is a web service that exposes an HTTP API for inference requests. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the endpoint.
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Deploy a Model on Amazon SageMaker
AWS Machine Learning Training - Use Your Own Inference Code with Amazon SageMaker Hosting Services

NEW QUESTION # 175
A large company has developed a B1 application that generates reports and dashboards using data collected from various operational metrics The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports The company wants the executives to be able ask questions using written and spoken interlaces Which combination of services can be used to build this conversational interface? (Select THREE)

A. Alexa for Business
B. Amazon Transcribe
C. Amazon Comprehend
D. Amazon Lex
E. Amazon Connect
F. Amazon Poly

Answer: B,C,D

Explanation:
To build a conversational interface that can use natural language to get data from the reports, the company can use a combination of services that can handle both written and spoken inputs, understand the user's intent and query, and extract the relevant information from the reports. The services that can be used for this purpose are:
Amazon Lex: A service for building conversational interfaces into any application using voice and text.
Amazon Lex can create chatbots that can interact with users using natural language, and integrate with other AWS services such as Amazon Connect, Amazon Comprehend, and Amazon Transcribe. Amazon Lex can also use lambda functions to implement the business logic and fulfill the user's requests.
Amazon Comprehend: A service for natural language processing and text analytics. Amazon Comprehend can analyze text and speech inputs and extract insights such as entities, key phrases, sentiment, syntax, and topics.
Amazon Comprehend can also use custom classifiers and entity recognizers to identify specific terms and concepts that are relevant to the domain of the reports.
Amazon Transcribe: A service for speech-to-text conversion. Amazon Transcribe can transcribe audio inputs into text outputs, and add punctuation and formatting. Amazon Transcribe can also use custom vocabularies and language models to improve the accuracy and quality of the transcription for the specific domain of the reports.
Therefore, the company can use the following architecture to build the conversational interface:
Use Amazon Lex to create a chatbot that can accept both written and spoken inputs from the executives. The chatbot can use intents, utterances, and slots to capture the user's query and parameters, such as the report name, date, metric, or filter.
Use Amazon Transcribe to convert the spoken inputs into text outputs, and pass them to Amazon Lex.
Amazon Transcribe can use a custom vocabulary and language model to recognize the terms and concepts related to the reports.
Use Amazon Comprehend to analyze the text inputs and outputs, and extract the relevant information from the reports. Amazon Comprehend can use a custom classifier and entity recognizer to identify the report name, date, metric, or filter from the user's query, and the corresponding data from the reports.
Use a lambda function to implement the business logic and fulfillment of the user's query, such as retrieving the data from the reports, performing calculations or aggregations, and formatting the response. The lambda function can also handle errors and validations, and provide feedback to the user.
Use Amazon Lex to return the response to the user, either in text or speech format, depending on the user's preference.
What Is Amazon Lex?
What Is Amazon Comprehend?
What Is Amazon Transcribe?

NEW QUESTION # 176
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. 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
B. Use AWS Lambda to run a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket.
C. Use Apache Airflow to orchestrate a set of predefined transformations on each new dataset that arrives in the S3 bucket.
D. Configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket.

Answer: D

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 # 177
A company is using Amazon SageMaker to build a machine learning (ML) model to predict customer churn based on customer call transcripts. Audio files from customer calls are located in an on-premises VoIP system that has petabytes of recorded calls. The on-premises infrastructure has high-velocity networking and connects to the company's AWS infrastructure through a VPN connection over a 100 Mbps connection.
The company has an algorithm for transcribing customer calls that requires GPUs for inference. The company wants to store these transcriptions in an Amazon S3 bucket in the AWS Cloud for model development.
Which solution should an ML specialist use to deliver the transcriptions to the S3 bucket as quickly as possible?

A. Order and use AWS Outposts to run the transcription algorithm on GPU-based Amazon EC2 instances.
Store the resulting transcriptions in the transcription S3 bucket.
B. Use AWS DataSync to ingest the audio files to Amazon S3. Create an AWS Lambda function to run the transcription algorithm on the audio files when they are uploaded to Amazon S3. Configure the function to write the resulting transcriptions to the transcription S3 bucket.
C. Order and use an AWS Snowcone device with Amazon EC2 Inf1 instances to run the transcription algorithm Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket
D. Order and use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.

Answer: D

Explanation:
Explanation
The company needs to transcribe petabytes of audio files from an on-premises VoIP system to an S3 bucket in the AWS Cloud. The transcription algorithm requires GPUs for inference, which are not available on the on-premises system. The VPN connection over a 100 Mbps connection is not sufficient to transfer the large amount of data quickly. Therefore, the company should use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm locally and leverage the GPU power.
The device can store up to 42 TB of data and can be shipped back to AWS for data ingestion. The company can use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket in the AWS Cloud.
This solution minimizes the network bandwidth and latency issues and enables faster data processing and transfer.
Option B is incorrect because AWS Snowcone is a small, portable, rugged, and secure edge computing and data transfer device that can store up to 8 TB of data. It is not suitable for processing petabytes of data and does not support GPU-based instances.
Option C is incorrect because AWS Outposts is a service that extends AWS infrastructure, services, APIs, and tools to virtually any data center, co-location space, or on-premises facility. It is not designed for data transfer and ingestion, and it would require additional infrastructure and maintenance costs.
Option D is incorrect because AWS DataSync is a service that makes it easy to move large amounts of data to and from AWS over the internet or AWS Direct Connect. However, using DataSync to ingest the audio files to S3 would still be limited by the network bandwidth and latency. Moreover, running the transcription algorithm on AWS Lambda would incur additional costs and complexity, and it would not leverage the GPU power that the algorithm requires.
References:
AWS Snowball Edge Compute Optimized
AWS DataSync
AWS Snowcone
AWS Outposts
AWS Lambda

NEW QUESTION # 178
While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown What does this mean?

A. There are too many variables in the model
B. The dataset cannot be accurately represented using the regression model
C. The model is predicting its target values perfectly.
D. The model might have prediction errors over a range of target values.

Answer: C

NEW QUESTION # 179
......

Are you on the way to pass the MLS-C01 exam? Our MLS-C01 exam questions will be the best choice for you. And if you still feel uncertain about the content, wondering whether it is the exact MLS-C01 exam material that you want, you can free download the demo to check it out. You will be quite surprised by the convenience to have an overview just by clicking into the link, and you can experience all kinds of MLS-C01 versions.

MLS-C01 Valid Exam Cram: https://www.itexamreview.com/MLS-C01-exam-dumps.html

BTW, DOWNLOAD part of ITexamReview MLS-C01 dumps from Cloud Storage: https://drive.google.com/open?id=1W6mJV0zRq0tCUFoEQ5wnheMNPb4ZhKSY