AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY TRAINING PDF - AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY RELIABLE TEST PATTERN

AWS-Certified-Machine-Learning-Specialty Training Pdf - AWS-Certified-Machine-Learning-Specialty Reliable Test Pattern

AWS-Certified-Machine-Learning-Specialty Training Pdf - AWS-Certified-Machine-Learning-Specialty Reliable Test Pattern

Blog Article

Tags: AWS-Certified-Machine-Learning-Specialty Training Pdf, AWS-Certified-Machine-Learning-Specialty Reliable Test Pattern, AWS-Certified-Machine-Learning-Specialty Trustworthy Exam Content, AWS-Certified-Machine-Learning-Specialty Exam Sample Online, AWS-Certified-Machine-Learning-Specialty Latest Test Simulations

BONUS!!! Download part of Exam4Labs AWS-Certified-Machine-Learning-Specialty dumps for free: https://drive.google.com/open?id=1g8E7-9j5jIw1_xFcUEyyEcaGwhcmhjnn

Do you want to double your salary in a short time? Yes, it is not a dream. Our AWS-Certified-Machine-Learning-Specialty latest study guide can help you. IT field is becoming competitive; a Amazon certification can help you do that. If you get a certification with our AWS-Certified-Machine-Learning-Specialty latest study guide, maybe your career will change. A useful certification will bring you much outstanding advantage when you apply for any jobs about Amazon company or products. Just only dozens of money on AWS-Certified-Machine-Learning-Specialty Latest Study Guide will assist you 100% pass exam and 24-hours worm aid service.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Certification Exam is a highly sought-after certification that recognizes a person's skills and knowledge in the field of machine learning on the Amazon Web Services (AWS) platform. AWS-Certified-Machine-Learning-Specialty Exam is intended for IT professionals who want to demonstrate their expertise in designing, deploying and managing machine learning solutions on AWS.

>> AWS-Certified-Machine-Learning-Specialty Training Pdf <<

AWS-Certified-Machine-Learning-Specialty Reliable Test Pattern & AWS-Certified-Machine-Learning-Specialty Trustworthy Exam Content

As far as the AWS Certified Machine Learning - Specialty (AWS-Certified-Machine-Learning-Specialty) exam questions are concerned, these Amazon AWS-Certified-Machine-Learning-Specialty exam questions are designed and verified by the experience and qualified AWS-Certified-Machine-Learning-Specialty exam trainers. They work together and strive hard to maintain the top standard of AWS-Certified-Machine-Learning-Specialty Exam Practice questions all the time. So you rest assured that with the Exam4Labs Amazon AWS-Certified-Machine-Learning-Specialty exam questions you will ace your AWS-Certified-Machine-Learning-Specialty exam preparation and feel confident to solve all questions in the final Amazon AWS-Certified-Machine-Learning-Specialty exam.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q110-Q115):

NEW QUESTION # 110
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. 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.
  • B. 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
  • C. 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.
  • 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 # 111
An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?

  • A. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
  • B. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
  • C. Amazon Transcribe, Amazon Translate, and Amazon Comprehend
  • D. Amazon Transcribe, Amazon Translate, and Amazon SageMaker BlazingText

Answer: C

Explanation:
Amazon Transcribe, Amazon Translate, and Amazon Comprehend are the most efficient combination of services to accomplish the task of sentiment analysis on a video clip with audio in Spanish. Amazon Transcribe is a service that can convert speech to text using deep learning. Amazon Transcribe can transcribe audio from various sources, such as video files, audio files, or streaming audio. Amazon Transcribe can also recognize multiple speakers, different languages, accents, dialects, and custom vocabularies. In this case, Amazon Transcribe can transcribe the audio from the video clip in Spanish to text in Spanish1 Amazon Translate is a service that can translate text from one language to another using neural machine translation. Amazon Translate can translate text from various sources, such as documents, web pages, chat messages, etc. Amazon Translate can also support multiple languages, domains, and styles. In this case, Amazon Translate can translate the text from Spanish to English2 Amazon Comprehend is a service that can analyze and derive insights from text using natural language processing. Amazon Comprehend can perform various tasks, such as sentiment analysis, entity recognition, key phrase extraction, topic modeling, etc. Amazon Comprehend can also support multiple languages and domains. In this case, Amazon Comprehend can perform sentiment analysis on the text in English and determine whether the feedback is positive, negative, neutral, or mixed3 The other options are not valid or efficient for accomplishing the task of sentiment analysis on a video clip with audio in Spanish. Amazon Comprehend, Amazon SageMaker seq2seq, and Amazon SageMaker Neural Topic Model (NTM) are not a good combination, as they do not include a service that can transcribe speech to text, which is a necessary step for processing the audio from the video clip. Amazon Comprehend, Amazon Translate, and Amazon SageMaker BlazingText are not a good combination, as they do not include a service that can perform sentiment analysis, which is the main goal of the task. Amazon SageMaker BlazingText is a service that can train and deploy text classification and word embedding models using deep learning. Amazon SageMaker BlazingText can perform tasks such as text classification, named entity recognition, part-of-speech tagging, etc., but not sentiment analysis4


NEW QUESTION # 112
A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Choose two.)

  • A. AWS CloudTrail
  • B. AWS Health
  • C. AWS Config
  • D. Amazon CloudWatch
  • E. AWS Trusted Advisor

Answer: A,D

Explanation:
https://aws.amazon.com/sagemaker/faqs/


NEW QUESTION # 113
A network security vendor needs to ingest telemetry data from thousands of endpoints that run all over the world. The data is transmitted every 30 seconds in the form of records that contain 50 fields. Each record is up to 1 KB in size. The security vendor uses Amazon Kinesis Data Streams to ingest the data. The vendor requires hourly summaries of the records that Kinesis Data Streams ingests. The vendor will use Amazon Athena to query the records and to generate the summaries. The Athena queries will target 7 to 12 of the available data fields.
Which solution will meet these requirements with the LEAST amount of customization to transform and store the ingested data?

  • A. Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using AWS Lambda.
  • B. Use Amazon Kinesis Data Analytics to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.
  • C. Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using a short-lived Amazon EMR cluster.
  • D. Use AWS Lambda to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.

Answer: B

Explanation:
Explanation
The solution that will meet the requirements with the least amount of customization to transform and store the ingested data is to use Amazon Kinesis Data Analytics to read and aggregate the data hourly, transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose. This solution leverages the built-in features of Kinesis Data Analytics to perform SQL queries on streaming data and generate hourly summaries.
Kinesis Data Analytics can also output the transformed data to Kinesis Data Firehose, which can then deliver the data to S3 in a specified format and partitioning scheme. This solution does not require any custom code or additional infrastructure to process the data. The other solutions either require more customization (such as using Lambda or EMR) or do not meet the requirement of aggregating the data hourly (such as using Lambda to read the data from Kinesis Data Streams). References:
1: Boosting Resiliency with an ML-based Telemetry Analytics Architecture | AWS Architecture Blog
2: AWS Cloud Data Ingestion Patterns and Practices
3: IoT ingestion and Machine Learning analytics pipeline with AWS IoT ...
4: AWS IoT Data Ingestion Simplified 101: The Complete Guide - Hevo Data


NEW QUESTION # 114
A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?

  • A. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
  • B. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
  • C. AWS Glue with a custom ETL script to transform the data.
  • D. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

Answer: B

Explanation:
Amazon Kinesis Data Analytics is a service that enables you to analyze streaming data in real time using SQL or Apache Flink applications. You can use Kinesis Data Analytics to process and gain insights from data streams such as web logs, clickstreams, IoT data, and more.
To use SQL to query a data stream of GZIP files, you need to first transform the data into a format that Kinesis Data Analytics can understand, such as JSON, CSV, or Apache Parquet. You can use an AWS Lambda function to perform this transformation and send the output to a Kinesis data stream that is connected to your Kinesis Data Analytics application. This way, you can use SQL to query the stream with the least latency, as Lambda functions are triggered in near real time by the incoming data and Kinesis Data Analytics can process the data as soon as it arrives.
The other options are not optimal for this scenario, as they introduce more latency or complexity. AWS Glue is a serverless data integration service that can perform ETL (extract, transform, and load) tasks on data sources, but it is not designed for real-time streaming data analysis. An Amazon Kinesis Client Library is a Java library that enables you to build custom applications that process data from Kinesis data streams, but it requires more coding and configuration than using a Lambda function. Amazon Kinesis Data Firehose is a service that can deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, but it does not support SQL queries on the data.
References:
What Is Amazon Kinesis Data Analytics for SQL Applications?
Using AWS Lambda with Amazon Kinesis Data Streams
Using AWS Lambda with Amazon Kinesis Data Firehose


NEW QUESTION # 115
......

By selecting our AWS-Certified-Machine-Learning-Specialty study materials, you do not need to purchase any other products. Our passing rate may be the most attractive factor for you. Our AWS-Certified-Machine-Learning-Specialty learning guide have a 99% pass rate. This shows what? As long as you use our products, you can pass the exam! Do you want to be one of 99? Quickly purchase our AWS-Certified-Machine-Learning-Specialty Exam Questions! And you will find that the coming exam is just a piece of cake in front of you.

AWS-Certified-Machine-Learning-Specialty Reliable Test Pattern: https://www.exam4labs.com/AWS-Certified-Machine-Learning-Specialty-practice-torrent.html

DOWNLOAD the newest Exam4Labs AWS-Certified-Machine-Learning-Specialty PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=1g8E7-9j5jIw1_xFcUEyyEcaGwhcmhjnn

Report this page