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MLS-C01 資料勉強、 Amazon MLS-C01 過去問題 & AWS Certified Machine Learning Specialty - Royalholidayclubbed
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Royalholidayclubbedを選んだら、成功への扉を開きます。頑張ってください。人生にはあまりにも多くの変化および未知の誘惑がありますから、まだ若いときに自分自身のために強固な基盤を築くべきです。 君が後悔しないようにもっと少ないお金を使って大きな良い成果を取得するためにRoyalholidayclubbedを選択してください。Royalholidayclubbedはまた一年間に無料なサービスを更新いたします。 それに、Royalholidayclubbedの教材を購入すれば、Royalholidayclubbedは一年間の無料アップデート・サービスを提供してあげます。
AWS Certified Specialty MLS-C01 常々、時間とお金ばかり効果がないです。
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それはあなたが成功認定を助ける良いヘルパーですから、あなたはまだ何を待っているのですか。速く最新のRoyalholidayclubbedのAmazonのMLS-C01資料勉強トレーニング資料を取りに行きましょう。現在、IT業界での激しい競争に直面しているあなたは、無力に感じるでしょう。
Amazon MLS-C01資料勉強 - 自分の幸せは自分で作るものだと思われます。
AmazonのMLS-C01資料勉強認定試験に受かるためにがんばって勉強していれば、Royalholidayclubbedはあなたにヘルプを与えます。Royalholidayclubbed が提供したAmazonのMLS-C01資料勉強問題集は実践の検査に合格したもので、最も良い品質であなたがAmazonのMLS-C01資料勉強認定試験に合格することを保証します。
あなたは弊社の高品質Amazon MLS-C01資料勉強試験資料を利用して、一回に試験に合格します。RoyalholidayclubbedのAmazon MLS-C01資料勉強問題集は専門家たちが数年間で過去のデータから分析して作成されて、試験にカバーする範囲は広くて、受験生の皆様のお金と時間を節約します。
MLS-C01 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 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: 3
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: 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
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