Omgzlookはきみの貴重な時間を節約するだけでなく、 安心で順調に試験に合格するのを保証します。Omgzlookは専門のIT業界での評判が高くて、あなたがインターネットでOmgzlookの部分のAmazon MLS-C01対応問題集「AWS Certified Machine Learning - Specialty」資料を無料でダウンロードして、弊社の正確率を確認してください。弊社の商品が好きなのは弊社のたのしいです。 OmgzlookのAmazonのMLS-C01対応問題集試験トレーニング資料は間違いなく最高のトレーニング資料ですから、それを選ぶことはあなたにとって最高の選択です。IT専門家になりたいですか。 Omgzlook を選択して１００％の合格率を確保することができて、もし試験に失敗したら、Omgzlookが全額で返金いたします。
AmazonのMLS-C01対応問題集試験に合格するのは最良の方法の一です。
あるいは、無料で試験MLS-C01 - AWS Certified Machine Learning - Specialty対応問題集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MLS-C01 技術試験試験の資料のいくつかのバーションのデモは我々のウェブサイトで無料でダウンロードできます。あなたの愛用する版をやってみよう。
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MLS-C01対応問題集試験トレーニング資料に注目していて、うちのAmazonのMLS-C01対応問題集問題集の高い正確性を保証するのです。
その他、Amazon MLS-C01対応問題集問題集の更新版を無料に提供します。
AmazonのMLS-C01対応問題集認定試験に合格することはきっと君の職業生涯の輝い将来に大変役に立ちます。Omgzlookを選ぶなら、君がAmazonのMLS-C01対応問題集認定試験に合格するということできっと喜んでいます。OmgzlookのAmazonのMLS-C01対応問題集問題集を購入するなら、君がAmazonのMLS-C01対応問題集認定試験に合格する率は１００パーセントです。あなたはOmgzlookの学習教材を購入した後、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。
その他、MLS-C01対応問題集試験認証証明書も仕事昇進にたくさんのメリットを与えられます。私たちの努力は自分の人生に更なる可能性を増加するためのことであるとよく思われます。
MLS-C01 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