MLS-C01 受験記 - MLS-C01 ミシュレーション問題 & AWS Certified Machine Learning Specialty - Omgzlook

Omgzlookが提供したAmazonのMLS-C01受験記トレーニング資料を利用したら、AmazonのMLS-C01受験記認定試験に受かることはたやすくなります。Omgzlookがデザインしたトレーニングツールはあなたが一回で試験に合格することにヘルプを差し上げられます。OmgzlookのAmazonのMLS-C01受験記トレーニング資料即ち問題と解答をダウンロードする限り、気楽に試験に受かることができるようになります。 AmazonのMLS-C01受験記試験を申し込むのは賢明な選択で今のは競争の激しいIT業界では、絶えず自分を高めるべきです。しかし多くの選択肢があるので君はきっと悩んでいましょう。 あなたが自分のキャリアでの異なる条件で自身の利点を発揮することを助けられます。

その中で、MLS-C01受験記認定試験は最も重要な一つです。

その団体はAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty受験記の認証試験の最新の資料に専攻して、あなたが気楽にAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty受験記の認証試験に合格するためにがんばっています。 まだ何を待っていますか。早速買いに行きましょう。

AmazonのMLS-C01受験記認定試験を受けることを決めたら、Omgzlookがそばにいて差し上げますよ。Omgzlookはあなたが自分の目標を達成することにヘルプを差し上げられます。あなたがAmazonのMLS-C01受験記「AWS Certified Machine Learning - Specialty」認定試験に合格する需要を我々はよく知っていますから、あなたに高品質の問題集と科学的なテストを提供して、あなたが気楽に認定試験に受かることにヘルプを提供するのは我々の約束です。

Amazon MLS-C01受験記 - それはあなたが夢を実現することを助けられます。

努力する人生と努力しない人生は全然違いますなので、あなたはのんびりした生活だけを楽しみしていき、更なる進歩を求めるのではないか?スマートを一方に置いて、我々AmazonのMLS-C01受験記試験問題集をピックアップします。弊社のMLS-C01受験記試験問題集によって、あなたの心と精神の満足度を向上させながら、勉強した後MLS-C01受験記試験資格認定書を受け取って努力する人生はすばらしいことであると認識られます。

さて、はやく試験を申し込みましょう。Omgzlookはあなたを助けることができますから、心配する必要がないですよ。

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