Omgzlookは君の成功のために、最も質の良いAmazonのMLS-C01勉強ガイド試験問題と解答を提供します。もし君はいささかな心配することがあるなら、あなたはうちの商品を購入する前に、Omgzlookは無料でサンプルを提供することができます。あなたはOmgzlookのAmazonのMLS-C01勉強ガイド問題集を購入した後、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。 人によって目標が違いますが、あなたにAmazon MLS-C01勉強ガイド試験に順調に合格できるのは我々の共同の目標です。この目標の達成はあなたがIT技術領域へ行く更なる発展の一歩ですけど、我々社Omgzlook存在するこそすべての意義です。 弊社の商品はあなたの圧力を減少できます。
AWS Certified Specialty MLS-C01 Omgzlookには専門的なエリート団体があります。
そのほかに、MLS-C01 - AWS Certified Machine Learning - Specialty勉強ガイド試験の合格率は高い、多くの受験者が試験に合格しました。 OmgzlookのAmazonのMLS-C01 資料勉強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。それはあなたがいつでも最新の試験資料を持てるということです。
今の競争の激しいのIT業界の中にAmazon MLS-C01勉強ガイド認定試験に合格して、自分の社会地位を高めることができます。弊社のIT業で経験豊富な専門家たちが正確で、合理的なAmazon MLS-C01勉強ガイド「AWS Certified Machine Learning - Specialty」認証問題集を作り上げました。 弊社の勉強の商品を選んで、多くの時間とエネルギーを節約こともできます。
Amazon MLS-C01勉強ガイド - そして、試験を安心に参加してください。
常々、時間とお金ばかり効果がないです。正しい方法は大切です。我々Omgzlookは一番効果的な方法を探してあなたにAmazonのMLS-C01勉強ガイド試験に合格させます。弊社のAmazonのMLS-C01勉強ガイド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。購入した前の無料の試み、購入するときのお支払いへの保障、購入した一年間の無料更新AmazonのMLS-C01勉強ガイド試験に失敗した全額での返金…これらは我々のお客様への承諾です。
弊社のMLS-C01勉強ガイドのトレーニング資料を買ったら、一年間の無料更新サービスを差し上げます。もっと長い時間をもらって試験を準備したいのなら、あなたがいつでもサブスクリプションの期間を伸びることができます。
MLS-C01 PDF DEMO:
QUESTION NO: 1
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: 2
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: 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 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: 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