長年にわたり、Goldmile-InfobizはずっとIT認定試験を受験する皆さんに最良かつ最も信頼できる参考資料を提供するために取り組んでいます。IT認定試験の出題範囲に対して、Goldmile-Infobizは豊富な経験を持っています。また、Goldmile-Infobizは数え切れない受験生を助け、皆さんの信頼と称賛を得ました。 ほかのホームページに弊社みたいな問題集を見れば、あとでみ続けて、弊社の商品を盗作することとよくわかります。Goldmile-Infobizが提供した資料は最も全面的で、しかも更新の最も速いです。 こんなに重要な試験ですから、あなたも受験したいでしょう。
AWS Certified Specialty MLS-C01 いろいろな受験生に通用します。
Goldmile-InfobizのAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty問題数トレーニング資料を利用しても合格しないのなら、我々は全額で返金することができます。 AmazonのMLS-C01 資格勉強ソフトを使用するすべての人を有効にするために最も快適なレビュープロセスを得ることができ、我々は、AmazonのMLS-C01 資格勉強の資料を提供し、PDF、オンラインバージョン、およびソフトバージョンを含んでいます。あなたの愛用する版を利用して、あなたは簡単に最短時間を使用してAmazonのMLS-C01 資格勉強試験に合格することができ、あなたのIT機能を最も権威の国際的な認識を得ます！
IT業種は急激に発展しているこの時代で、IT専門家を称賛しなければならないです。彼らは自身が持っている先端技術で色々な便利を作ってくれます。それに、会社に大量な人的·物的資源を節約させると同時に、案外のうまい効果を取得しました。
Amazon MLS-C01問題数 - 無事試験に合格しました。
IT認定試験は現在の社会で、特にIT業界で最も人気のある試験だと考えられています。IT認定試験の認証資格は国際社会で広く認可されています。昇進したく、昇給したく、あるいは単に自分の仕事スキルを向上させたいなら、IT認定試験を受験して資格を取得するのはあなたの最もよい選択です。どうですか。あなたもきっとそう思うでしょう。ですから、躊躇しないではやく試験を申し込みましょう。AmazonのMLS-C01問題数認定試験は最近最も人気のある試験ですから、受験したいのですか。試験に準備する方法がわからない場合、Goldmile-Infobizは教えてあげます。Goldmile-Infobizで、あなたは試験に関するすべての優れた参考書を見つけることができます。
我々Goldmile-InfobizのAmazon MLS-C01問題数試験問題と試験解答の正確さは、あなたの試験準備をより簡単にし、あなたが試験に高いポイントを得ることを保証します。Amazon MLS-C01問題数資格試験に参加する意向があれば、当社のGoldmile-Infobizから自分に相応しい受験対策解説集を選らんで、認定試験の学習教材として勉強します。
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 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