君は他の人の一半の努力で、同じAmazonのMLS-C01資料的中率認定試験を簡単に合格できます。Goldmile-Infobizはあなたと一緒に君のITの夢を叶えるために頑張ります。まだなにを待っていますか。 Goldmile-Infobizの学習教材は君の初めての試しでAmazonのMLS-C01資料的中率認定試験に合格するのに助けます。Goldmile-InfobizのAmazonのMLS-C01資料的中率試験トレーニング資料を利用すれば、認定試験に合格するのは簡単になります。 我々Goldmile-Infobizは最高のアフターサービスを提供いたします。
彼らにAmazonのMLS-C01資料的中率試験に合格させました。
我々はあなたに提供するのは最新で一番全面的なAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty資料的中率問題集で、最も安全な購入保障で、最もタイムリーなAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty資料的中率試験のソフトウェアの更新です。 何十ユーロだけでこのような頼もしいAmazonのMLS-C01 受験対策解説集試験の資料を得ることができます。試験に合格してからあなたがよりよい仕事と給料がもらえるかもしれません。
AmazonのMLS-C01資料的中率試験に合格するのは難しいですが、合格できるのはあなたの能力を証明できるだけでなく、国際的な認可を得られます。AmazonのMLS-C01資料的中率試験の準備は重要です。我々Goldmile-Infobizの研究したAmazonのMLS-C01資料的中率の復習資料は科学的な方法であなたの圧力を減少します。
Amazon MLS-C01資料的中率 - 準備の段階であなたはリーダーしています。
Amazon MLS-C01資料的中率資格認定はIT技術領域に従事する人に必要があります。我々社のAmazon MLS-C01資料的中率試験練習問題はあなたに試験うま合格できるのを支援します。あなたの取得したAmazon MLS-C01資料的中率資格認定は、仕事中に核心技術知識を同僚に認可されるし、あなたの技術信頼度を増強できます。
幸せの生活は自分で作られて得ることです。だから、大人気なIT仕事に従事したいあなたは今から準備して努力するのではないでしょうか？さあ、ここで我々社のAmazonのMLS-C01資料的中率試験模擬問題を推薦させてくださいませんか。
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 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