彼らはGoldmile-Infobizの問題集が有効なこと確認しました。Goldmile-Infobizが提供しておりますのは専門家チームの研究した問題と真題で弊社の高い名誉はたぶり信頼をうけられます。安心で弊社の商品を使うために無料なサンブルをダウンロードしてください。 勉強中で、何の質問があると、メールで我々はあなたのためにすぐ解決します。心配はありませんし、一心不乱に試験復習に取り組んでいます。 Goldmile-Infobizは専門家チームが自分の知識と経験をを利用してAmazonのMLS-C01日本語版トレーリング「AWS Certified Machine Learning - Specialty」認証試験の問題集を研究したものでございます。
AWS Certified Specialty MLS-C01 何の問題があったらお気軽に聞いてください。
Goldmile-InfobizのAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty日本語版トレーリングトレーニング資料を持っていたら、自信を持つようになります。 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。
Goldmile-InfobizのAmazonのMLS-C01日本語版トレーリング試験トレーニング資料は私達受験生の最良の選択です。最もリラックスした状態ですべての苦難に直面しています。AmazonのMLS-C01日本語版トレーリング「AWS Certified Machine Learning - Specialty」試験はとても難しいですが、受験生の皆がリラックスした状態で試験を受けるべきです。
Amazon MLS-C01日本語版トレーリング - あなたは最高のトレーニング資料を手に入れました。
Goldmile-InfobizのAmazonのMLS-C01日本語版トレーリング試験トレーニング資料は全てのオンラインのトレーニング資料で一番よいものです。我々の知名度はとても高いです。これは受験生の皆さんが資料を利用した後の結果です。Goldmile-InfobizのAmazonのMLS-C01日本語版トレーリング試験トレーニング資料を選んだら、１００パーセントの成功率を保証します。もし失敗だったら、我々は全額で返金します。受験生の皆さんの重要な利益が保障できるようにGoldmile-Infobizは絶対信頼できるものです。
自信を持っていないからMLS-C01日本語版トレーリング試験を受けるのは無理ですか。それは問題ではないですよ。
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 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: 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 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
QUESTION NO: 5
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
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Updated: May 28, 2022