AWS-Certified-Machine-Learning-Specialty日本語復習赤本 & AWS-Certified-Machine-Learning-Specialty出題内容 - AWS-Certified-Machine-Learning-Specialty最新知識 - Goldmile-Infobiz

弊社が提供した問題集がほかのインターネットに比べて問題のカーバ範囲がもっと広くて対応性が強い長所があります。Goldmile-Infobizが持つべきなIT問題集を提供するサイトでございます。 もし君が試験に関する問題があれば、私たちは最も早い時間で、解答します。一日も早くAmazonのAWS-Certified-Machine-Learning-Specialty日本語復習赤本試験に合格したい？ Goldmile-Infobizが提供した問題と解答はIT領域のエリートたちが研究して、実践して開発されたものです。 Goldmile-Infobizが提供するAmazonのAWS-Certified-Machine-Learning-Specialty日本語復習赤本認証試験問題集が君の試験に合格させます。

AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty あなたの成功も我々Goldmile-Infobizの成功です。

AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty日本語復習赤本 - AWS Certified Machine Learning - Specialty これは全てのIT認証試験を受ける受験生のアドバイスです。 ほかのたくさんの受験生は生活の中でのことに挑戦しています。だから、我々は受験生の皆さんに一番効果的なAmazonのAWS-Certified-Machine-Learning-Specialty 過去問無料復習方法を提供します。

優秀な試験参考書は話すことに依頼することでなく、受験生の皆さんに検証されることに依頼するのです。Goldmile-Infobizの参考資料は時間の試練に耐えることができます。Goldmile-Infobizは現在の実績を持っているのは受験生の皆さんによって実践を通して得られた結果です。

Amazon AWS-Certified-Machine-Learning-Specialty日本語復習赤本 - 自分の幸せは自分で作るものだと思われます。

Goldmile-Infobizは長年にわたってずっとIT認定試験に関連するAWS-Certified-Machine-Learning-Specialty日本語復習赤本参考書を提供しています。これは受験生の皆さんに検証されたウェブサイトで、一番優秀な試験AWS-Certified-Machine-Learning-Specialty日本語復習赤本問題集を提供することができます。Goldmile-Infobizは全面的に受験生の利益を保証します。皆さんからいろいろな好評をもらいました。しかも、Goldmile-Infobizは当面の市場で皆さんが一番信頼できるサイトです。

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AWS-Certified-Machine-Learning-Specialty 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