MLS-C01 全真問題集 & Amazon AWS Certified Machine Learning Specialty 対応内容 - Goldmile-Infobiz

この競争が激しい社会では、Goldmile-Infobizはたくさんの受験生の大好評を博するのは我々はいつも受験生の立場で試験ソフトを開発するからです。例えば、我々のよく発売されているAmazonのMLS-C01全真問題集試験ソフトは大量の試験問題への研究によって作れることです。試験に失敗したら全額で返金するという承諾があるとは言え、弊社の商品を利用したほとんどの受験生は試験に合格しました。 一年間のソフト無料更新も失敗して全額での返金も我々の誠のアフターサービスでございます。弊社のGoldmile-InfobizはAmazonのMLS-C01全真問題集試験を準備している人々に保障を提供しています。 IT業界の発展とともに、IT業界で働いている人への要求がますます高くなります。

MLS-C01全真問題集練習資料が最も全面的な参考書です。

我々社のMLS-C01 - AWS Certified Machine Learning - Specialty全真問題集問題集を参考した後、ほっとしました。 Amazon MLS-C01 日本語解説集認証試験に合格することが簡単ではなくて、Amazon MLS-C01 日本語解説集証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。

我々社のAmazon MLS-C01全真問題集認定試験問題集の合格率は高いのでほとんどの受験生はMLS-C01全真問題集認定試験に合格するのを保証します。もしあなたはAmazon MLS-C01全真問題集試験問題集に十分な注意を払って、MLS-C01全真問題集試験の解答を覚えていれば、MLS-C01全真問題集認定試験の成功は明らかになりました。Amazon MLS-C01全真問題集模擬問題集で実際の質問と正確の解答に疑問があれば、無料の練習問題集サンプルをダウンロードし、チェックしてください。

Amazon MLS-C01全真問題集 - もし合格しないと、われは全額で返金いたします。

AmazonのMLS-C01全真問題集認証試験を選んだ人々が一層多くなります。MLS-C01全真問題集試験がユニバーサルになりましたから、あなたはGoldmile-Infobiz のAmazonのMLS-C01全真問題集試験問題と解答¥を利用したらきっと試験に合格するができます。それに、あなたに極大な便利と快適をもたらせます。実践の検査に何度も合格したこのサイトは試験問題と解答を提供しています。皆様が知っているように、Goldmile-InfobizはAmazonのMLS-C01全真問題集試験問題と解答を提供している専門的なサイトです。

そしてあなたにMLS-C01全真問題集試験に関するテスト問題と解答が分析して差し上げるうちにあなたのIT専門知識を固めています。MLS-C01全真問題集「AWS Certified Machine Learning - Specialty」試験は簡単ではありません。

MLS-C01 PDF DEMO:

QUESTION NO: 1
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: 2
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: 3
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: 4
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

QUESTION NO: 5
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

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Updated: May 28, 2022