Goldmile-Infobizは受験者に向かって試験について問題を解決する受験資源を提供するサービスのサイトで、さまざまな受験生によって別のトレーニングコースを提供いたします。受験者はGoldmile-Infobizを通って順調に試験に合格する人がとても多くなのでGoldmile-InfobizがIT業界の中で高い名声を得ました。 専門的な知識が必要で、もしあなたはまだこの方面の知識を欠かれば、Goldmile-Infobizは君に向ける知識を提供いたします。Goldmile-Infobizの専門家チームは彼らの知識や経験を利用してあなたの知識を広めることを助けています。 AmazonのAWS-Certified-Machine-Learning-Specialty基礎訓練認定試験の最新教育資料はGoldmile-Infobizの専門チームが研究し続けてついに登場し、多くの人の夢が実現させることができます。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 何の問題があったらお気軽に聞いてください。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty基礎訓練 - AWS Certified Machine Learning - Specialty 弊社は君の試験の１００％合格率を保証いたします。 皆様が知っているように、Goldmile-InfobizはAmazonのAWS-Certified-Machine-Learning-Specialty 練習問題集試験問題と解答を提供している専門的なサイトです。AmazonのAWS-Certified-Machine-Learning-Specialty 練習問題集認証試験を選んだ人々が一層多くなります。
もし失敗したら、全額で返金を保証いたします。Goldmile-Infobizの問題集はIT専門家がAmazonのAWS-Certified-Machine-Learning-Specialty基礎訓練「AWS Certified Machine Learning - Specialty」認証試験について自分の知識と経験を利用して研究したものでございます。Goldmile-Infobizの問題集は真実試験の問題にとても似ていて、弊社のチームは自分の商品が自信を持っています。
Amazon AWS-Certified-Machine-Learning-Specialty基礎訓練 - 人生には様々な選択があります。
21世紀の情報化時代の急流の到来につれて、人々はこの時代に適応できるようにいつも自分の知識を増加していてますが、まだずっと足りないです。IT業種について言えば、AmazonのAWS-Certified-Machine-Learning-Specialty基礎訓練認定試験はIT業種で欠くことができない認証ですから、この試験に合格するのはとても必要です。この試験が難しいですから、試験に合格すれば国際的に認証され、受け入れられることができます。そうすると、美しい未来と高給をもらう仕事を持てるようになります。Goldmile-Infobizというサイトは世界で最も信頼できるIT認証トレーニング資料を持っていますから、Goldmile-Infobizを利用したらあなたがずっと期待している夢を実現することができるようになります。１００パーセントの合格率を保証しますから、AmazonのAWS-Certified-Machine-Learning-Specialty基礎訓練認定試験を受ける受験生のあなたはまだ何を待っているのですか。速くGoldmile-Infobizというサイトをクリックしてください。
このような保証があれば、Goldmile-InfobizのAWS-Certified-Machine-Learning-Specialty基礎訓練問題集を購入しようか購入するまいかと躊躇する必要は全くないです。この問題集をミスすればあなたの大きな損失ですよ。
AWS-Certified-Machine-Learning-Specialty PDF DEMO:
QUESTION NO: 1
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: 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 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: 4
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: 5
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
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Updated: May 28, 2022