AmazonのAWS-Certified-Machine-Learning-Specialty無料模擬試験ソフトを購入してから一年間の無料更新サービスも提供します。試験に失敗したら、全額で返金する承諾があります。だから、AmazonのAWS-Certified-Machine-Learning-Specialty無料模擬試験試験に合格したいあなたは安心で弊社の商品を選べばいいんです。 今の社会の中で、ネット上で訓練は普及して、弊社は試験問題集を提供する多くのネットの一つでございます。Omgzlookが提供したのオンライン商品がIT業界では品質の高い学習資料、受験生の必要が満足できるサイトでございます。 PDF、オンライン版とソフト版です。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 私の夢は最高のIT専門家になることです。
短時間で一度に本当の認定試験に高いポイントを取得したいなら、我々OmgzlookのAmazon AWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty無料模擬試験日本語対策問題集は絶対にあなたへの最善なオプションです。 あなたの夢は何ですか。あなたのキャリアでいくつかの輝かしい業績を行うことを望まないのですか。
そうすれば、わかりやすく、覚えやすいです。弊社の AWS-Certified-Machine-Learning-Specialty無料模擬試験参考資料は実践に基づいて、専門的な知識の蓄積です。だから、AWS-Certified-Machine-Learning-Specialty無料模擬試験試験のために、弊社の商品を選ばれば、後悔することがないです。
Amazon AWS-Certified-Machine-Learning-Specialty無料模擬試験 - 我々もオンライン版とソフト版を提供します。
IT領域での主要な問題が質と実用性が欠くということを我々ははっきり知っています。OmgzlookのAmazonのAWS-Certified-Machine-Learning-Specialty無料模擬試験の試験問題と解答はあなたが必要とした一切の試験トレーニング資料を準備して差し上げます。実際の試験のシナリオと一致で、选択問題（多肢選択問題）はあなたが試験を受かるために有効な助けになれます。OmgzlookのAmazonのAWS-Certified-Machine-Learning-Specialty無料模擬試験「AWS Certified Machine Learning - Specialty」の試験トレーニング資料は検証した試験資料で、Omgzlookの専門的な実践経験に含まれています。
我々OmgzlookはAmazonのAWS-Certified-Machine-Learning-Specialty無料模擬試験試験問題集をリリースする以降、多くのお客様の好評を博したのは弊社にとって、大変な名誉なことです。また、我々はさらに認可を受けられるために、皆様の一切の要求を満足できて喜ぶ気持ちでずっと協力し、完備かつ精確のAWS-Certified-Machine-Learning-Specialty無料模擬試験試験問題集を開発するのに準備します。
AWS-Certified-Machine-Learning-Specialty 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 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: 4
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: 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