Omgzlookには専門的なエリート団体があります。認証専門家や技術者及び全面的な言語天才がずっと最新のAmazonのAWS-Certified-Machine-Learning-Specialty過去問無料試験を研究していますから、AmazonのAWS-Certified-Machine-Learning-Specialty過去問無料認定試験に受かりたかったら、Omgzlookのサイトをクッリクしてください。あなたに成功に近づいて、夢の楽園に一歩一歩進めさせられます。 あなたがいつでも最新の試験資料を持っていることを保証します。Omgzlookは高品質の製品を提供するだけではなく、完全なアフターサービスも提供します。 OmgzlookのAmazonのAWS-Certified-Machine-Learning-Specialty過去問無料試験トレーニング資料を手に入れたら、我々は一年間の無料更新サービスを提供します。
それはもちろんOmgzlookのAWS-Certified-Machine-Learning-Specialty過去問無料問題集ですよ。
あるいは、無料で試験AWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty過去問無料問題集を更新してあげるのを選択することもできます。 AmazonのAWS-Certified-Machine-Learning-Specialty 最新問題問題集はAWS-Certified-Machine-Learning-Specialty 最新問題に関する問題をほとんど含まれます。私たちのAmazonのAWS-Certified-Machine-Learning-Specialty 最新問題問題集を使うのは君のベストな選択です。
なぜ受験生のほとんどはOmgzlookを選んだのですか。それはOmgzlookがすごく便利で、広い通用性があるからです。OmgzlookのITエリートたちは彼らの専門的な目で、最新的なAmazonのAWS-Certified-Machine-Learning-Specialty過去問無料試験トレーニング資料に注目していて、うちのAmazonのAWS-Certified-Machine-Learning-Specialty過去問無料問題集の高い正確性を保証するのです。
Amazon AWS-Certified-Machine-Learning-Specialty過去問無料 - 正しい方法は大切です。
我々は販売者とお客様の間の信頼が重要でもらい難いのを知っています。我々はAmazonのAWS-Certified-Machine-Learning-Specialty過去問無料ソフトであなたに専門と高効率を示して、最全面的な問題集と詳しい分析であなたに助けてAmazonのAWS-Certified-Machine-Learning-Specialty過去問無料試験に合格して、最高のサービスであなたの信頼を得ています。あなたが試験に合格するのは我々への一番よい評価です。
できるだけ100％の通過率を保証使用にしています。Omgzlookは多くの受験生を助けて彼らにAmazonのAWS-Certified-Machine-Learning-Specialty過去問無料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AWS-Certified-Machine-Learning-Specialty過去問無料試験を研究して解答を詳しく分析しますから。
AWS-Certified-Machine-Learning-Specialty PDF DEMO:
QUESTION NO: 1
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: 2
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: 3
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)
A. Decrease dropout.
B. Increase regularization.
C. Increase feature combinations.
D. Decrease feature combinations.
E. Decrease regularization.
F. Increase dropout.
Answer: A,B,C
QUESTION NO: 4
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: 5
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?
A. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data
Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations
B. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
C. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data
Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS
Glue to generate personalized product recommendations
Answer: C
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Updated: May 28, 2022