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AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 」と感謝します。
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Amazon AWS-Certified-Machine-Learning-Specialtyトレーニング資料 - 本当に助かりました。
今の多くのIT者が参加している試験に、AmazonのAWS-Certified-Machine-Learning-Specialtyトレーニング資料認定試験「AWS Certified Machine Learning - Specialty」がとても人気がある一つとして、合格するために豊富な知識と経験が必要です。AmazonのAWS-Certified-Machine-Learning-Specialtyトレーニング資料認定試験に準備する練習ツールや訓練機関に通学しなればまりませんでしょう。Goldmile-Infobizは君のもっともよい選択ですよ。多くIT者になりたい方にAmazonのAWS-Certified-Machine-Learning-Specialtyトレーニング資料認定試験に関する問題集を準備しております。君に短い時間に大量のITの専門知識を補充させています。
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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
Huawei H13-921_V1.5 - テストの時に有効なツルが必要でございます。 SAP C_ARSUM_2508 - 弊社の商品は試験の範囲を広くカバーすることが他のサイトがなかなか及ばならないです。 何千何万の登録された部門のフィードバックによって、それに大量な突っ込んだ分析を通じて、我々はどのサプライヤーがお客様にもっと新しいかつ高品質のMicrosoft MS-900-JPN資料を提供できるかを確かめる存在です。 Adobe AD0-E137 - 弊社が提供した問題集がほかのインターネットに比べて問題のカーバ範囲がもっと広くて対応性が強い長所があります。 Salesforce MCE-Admn-201認定試験の目標が変更されば、Goldmile-Infobizが提供した勉強資料も変化に追従して内容を変えます。
Updated: May 28, 2022