Goldmile-InfobizのAmazonのAWS-Certified-Machine-Learning-Specialty教育資料試験トレーニング資料は現在で一番人気があるダウンロードのフォーマットを提供します。PDFとソフトのフォーマットで、ダウンロードするのは易いです。Goldmile-Infobizが提供した製品がIT専門家は実際の経験を活かして作った最も良い製品で、あなたが自分の目標を達成するようにずっと一生懸命頑張っています。 Goldmile-Infobizの AmazonのAWS-Certified-Machine-Learning-Specialty教育資料試験トレーニング資料はGoldmile-Infobizの実力と豊富な経験を持っているIT専門家が研究したもので、本物のAmazonのAWS-Certified-Machine-Learning-Specialty教育資料試験問題とほぼ同じです。それを利用したら、君のAmazonのAWS-Certified-Machine-Learning-Specialty教育資料認定試験に合格するのは問題ありません。 それはコストパフォーマンスが非常に高い資料ですから、もしあなたも私と同じIT夢を持っていたら、Goldmile-InfobizのAmazonのAWS-Certified-Machine-Learning-Specialty教育資料試験トレーニング資料を利用してください。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 我々Goldmile-Infobizはこの3つを提供します。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty教育資料 - AWS Certified Machine Learning - Specialty 試験に合格してから、あなたのキャリアは美しい時期を迎えるようになります。 現在あなたに提供するのは大切なAmazonのAWS-Certified-Machine-Learning-Specialty トレーニング資料です。あなたの購入してから、我々はあなたにAmazonのAWS-Certified-Machine-Learning-Specialty トレーニング資料の更新情況をつど提供します。
そうすると、受験するとき、あなたは試験を容易に対処することができます。Goldmile-InfobizのAWS-Certified-Machine-Learning-Specialty教育資料問題集には、PDF版およびソフトウェア版のバージョンがあります。それはあなたに最大の利便性を与えることができます。
Amazon AWS-Certified-Machine-Learning-Specialty教育資料 - これは試験の一発合格を保証できる問題集ですから。
どのようにAmazon AWS-Certified-Machine-Learning-Specialty教育資料試験に準備すると悩んでいますか。我々社のAWS-Certified-Machine-Learning-Specialty教育資料問題集を参考した後、ほっとしました。弊社のAWS-Certified-Machine-Learning-Specialty教育資料ソフト版問題集はかねてより多くのIT事業をしている人々は順調にAmazon AWS-Certified-Machine-Learning-Specialty教育資料資格認定を取得させます。試験にパースする原因は我々問題集の全面的で最新版です。
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AWS-Certified-Machine-Learning-Specialty PDF DEMO:
QUESTION NO: 1
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: 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 built an image classification deep learning model. However the
Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and
75%r respectively.
How should the Specialist address this issue and what is the reason behind it?
A. The learning rate should be increased because the optimization process was trapped at a local minimum.
B. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
C. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
D. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
Answer: C
QUESTION NO: 4
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: 5
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
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Updated: May 28, 2022