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MLS-C01 ウェブトレーニング - MLS-C01 日本語関連対策 & AWS Certified Machine Learning Specialty - Royalholidayclubbed
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暇な時間だけでAmazonのMLS-C01ウェブトレーニング試験に合格したいのですか。我々の提供するPDF版のAmazonのMLS-C01ウェブトレーニング試験の資料はあなたにいつでもどこでも読めさせます。我々もオンライン版とソフト版を提供します。 AmazonのMLS-C01ウェブトレーニング試験はいくつ難しくても文句を言わないで、我々Royalholidayclubbedの提供する資料を通して、あなたはAmazonのMLS-C01ウェブトレーニング試験に合格することができます。AmazonのMLS-C01ウェブトレーニング試験を準備しているあなたに試験に合格させるために、我々Royalholidayclubbedは模擬試験ソフトを更新し続けています。 この小さい試すアクションはあなたが今までの最善のオプションであるかもしれません。
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Amazon MLS-C01ウェブトレーニング - Royalholidayclubbedを選んだら、成功への扉を開きます。
Royalholidayclubbedが提供したAmazonのMLS-C01ウェブトレーニングトレーニング資料はあなたが自分の夢を実現することを助けられます。 RoyalholidayclubbedはすべてのAmazonのMLS-C01ウェブトレーニング試験に受かるための資料に含まれていますから。Royalholidayclubbedを手にすると、あなたは安心に試験の準備に身を投げられるようになります。Royalholidayclubbedは高品質なトレーニング資料を提供して、あなたが試験に合格することを保証します。明るい未来を準備してあげます。
問題が更新される限り、Royalholidayclubbedは直ちに最新版のMLS-C01ウェブトレーニング資料を送ってあげます。そうすると、あなたがいつでも最新バージョンの資料を持っていることが保証されます。
MLS-C01 PDF DEMO:
QUESTION NO: 1
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: 2
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?
A. Receiver operating characteristic (ROC) curve
B. Misclassification rate
C. Root Mean Square Error (RM&)
D. L1 norm
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 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 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
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Updated: May 28, 2022
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