Goldmile-InfobizのAmazonのAWS-Certified-Machine-Learning-Specialty資格復習テキスト試験トレーニング資料は正確性が高くて、カバー率も広い。あなたがAmazonのAWS-Certified-Machine-Learning-Specialty資格復習テキスト認定試験に合格するのに最も良くて、最も必要な学習教材です。うちのAmazonのAWS-Certified-Machine-Learning-Specialty資格復習テキスト問題集を購入したら、私たちは一年間で無料更新サービスを提供することができます。 それで、弊社の専門家たちは多くの時間と精力を尽くし、Amazon AWS-Certified-Machine-Learning-Specialty資格復習テキスト試験資料を研究開発されます。なぜ我々社は試験に合格しないなら、全額での返金を承諾するのは大勢の客様が弊社のAmazon AWS-Certified-Machine-Learning-Specialty資格復習テキスト問題集を使用して試験に合格するのは我々に自信を与えるからです。 もしGoldmile-Infobizの学習教材を購入した後、どんな問題があれば、或いは試験に不合格になる場合は、私たちが全額返金することを保証いたします。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 我々Goldmile-Infobizはこの3つを提供します。
価格はちょっと高いですが、AWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty資格復習テキスト試験に最も有効な参考書です。 あなたの購入してから、我々はあなたにAmazonのAWS-Certified-Machine-Learning-Specialty 認証資格資料の更新情況をつど提供します。このサービスは無料なのです。
Goldmile-Infobizは正確な選択を与えて、君の悩みを減らして、もし早くてAmazon AWS-Certified-Machine-Learning-Specialty資格復習テキスト認証をとりたければ、早くてGoldmile-Infobizをショッピングカートに入れましょう。あなたにとても良い指導を確保できて、試験に合格するのを助けって、Goldmile-Infobizからすぐにあなたの通行証をとります。
Amazon AWS-Certified-Machine-Learning-Specialty資格復習テキスト - それは異なる世界の未来を意味しています。
Goldmile-Infobizが提供したAmazonのAWS-Certified-Machine-Learning-Specialty資格復習テキスト「AWS Certified Machine Learning - Specialty」試験問題と解答が真実の試験の練習問題と解答は最高の相似性があり、一年の無料オンラインの更新のサービスがあり、100%のパス率を保証して、もし試験に合格しないと、弊社は全額で返金いたします。
Goldmile-Infobizの勉強資料とAmazonのAWS-Certified-Machine-Learning-Specialty資格復習テキストに関する指導を従えば、初めてAmazonのAWS-Certified-Machine-Learning-Specialty資格復習テキスト認定試験を受けるあなたでも一回で試験に合格することができます。我々は受験生の皆様により高いスピードを持っているかつ効率的なサービスを提供することにずっと力を尽くしていますから、あなたが貴重な時間を節約することに助けを差し上げます。
AWS-Certified-Machine-Learning-Specialty 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