MLS-C01 技術内容 - Amazon MLS-C01 資格問題集 & AWS Certified Machine Learning Specialty - Royalholidayclubbed

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正しい方法は大切です。我々Royalholidayclubbedは一番効果的な方法を探してあなたにAmazonのMLS-C01技術内容試験に合格させます。弊社のAmazonのMLS-C01技術内容ソフトを購入するのを決めるとき、我々は各方面であなたに保障を提供します。 MLS-C01技術内容「AWS Certified Machine Learning - Specialty」はAmazonの一つ認証試験として、もしAmazon認証試験に合格してIT業界にとても人気があってので、ますます多くの人がMLS-C01技術内容試験に申し込んで、MLS-C01技術内容試験は簡単ではなくて、時間とエネルギーがかかって用意しなければなりません。 Royalholidayclubbedは多くの受験生を助けて彼らにAmazonのMLS-C01技術内容試験に合格させることができるのは我々専門的なチームがAmazonのMLS-C01技術内容試験を研究して解答を詳しく分析しますから。

AWS Certified Specialty MLS-C01 「成功っていうのはどちらですか。

Royalholidayclubbedの専門家チームがAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty技術内容認証試験に対して最新の短期有効なトレーニングプログラムを研究しました。もしIT認証の準備をしなかったら、あなたはのんびりできますか。もちろんです。

きっと君に失望させないと信じています。最新AmazonのMLS-C01技術内容認定試験は真実の試験問題にもっとも近くて比較的に全面的でございます。RoyalholidayclubbedのAmazonのMLS-C01技術内容認証試験について最新な研究を完成いたしました。

Amazon MLS-C01技術内容 - もちろんありますよ。

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MLS-C01 PDF DEMO:

QUESTION NO: 1
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?
A. Use Amazon Comprehend with the transcribed files to build the key topics
B. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
C. Use Amazon Translate with the transcribed files to train and build a model for the key topics
D. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics
Answer: C

QUESTION NO: 2
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. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
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. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
Answer: B

QUESTION NO: 3
A Machine Learning Specialist is using Amazon SageMaker to host a model for a highly available customer-facing application .
The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?
A. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.
B. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.
C. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.
D. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.
Answer: D

QUESTION NO: 4
A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?
A. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database
B. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database
C. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
D. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database
Answer: D

QUESTION NO: 5
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s) Which visualization will accomplish this?
A. A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor
Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.
B. A scatter plot showing (he performance of the objective metric over each training iteration
C. A histogram showing whether the most important input feature is Gaussian.
D. A scatter plot showing the correlation between maximum tree depth and the objective metric.
Answer: A

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Updated: May 28, 2022

by RHC.

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