我々は、失敗の言い訳ではなく、成功する方法を見つけます。あなたの利用するAmazonのAWS-Certified-Machine-Learning-Specialty難易度試験のソフトが最も権威的なのを保障するために、我々Goldmile-Infobizの専門家たちはAmazonのAWS-Certified-Machine-Learning-Specialty難易度試験の問題を研究して一番合理的な解答を整理します。AmazonのAWS-Certified-Machine-Learning-Specialty難易度試験の認証はあなたのIT能力への重要な証明で、あなたの就職生涯に大きな影響があります。 ここで私は明確にしたいのはGoldmile-InfobizのAWS-Certified-Machine-Learning-Specialty難易度問題集の核心価値です。Goldmile-Infobizの問題集は100％の合格率を持っています。 我々は多くの受験生にAmazonのAWS-Certified-Machine-Learning-Specialty難易度試験に合格させたことに自慢したことがないのです。
彼らにAmazonのAWS-Certified-Machine-Learning-Specialty難易度試験に合格させました。
AWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty難易度試験備考資料の整理を悩んでいますか。 何十ユーロだけでこのような頼もしいAmazonのAWS-Certified-Machine-Learning-Specialty 日本語版対応参考書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。
だから、Goldmile-InfobizはあなたがAmazon AWS-Certified-Machine-Learning-Specialty難易度資格認定試験にパースする正確の方法です。まだどうのようにAmazon AWS-Certified-Machine-Learning-Specialty難易度資格認定試験にパースすると煩悩していますか。現時点で我々サイトGoldmile-Infobizを通して、ようやくこの問題を心配することがありませんよ。
また、Amazon AWS-Certified-Machine-Learning-Specialty難易度問題集は的中率が高いです。
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Amazon AWS-Certified-Machine-Learning-Specialty難易度認証試験を通るために、いいツールが必要です。Amazon AWS-Certified-Machine-Learning-Specialty難易度認証試験について研究の資料がもっとも大部分になって、Goldmile-Infobizは早くてAmazon AWS-Certified-Machine-Learning-Specialty難易度認証試験の資料を集めることができます。
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