うちのAmazonのMLS-C01真実試験問題集を購入したら、私たちは一年間で無料更新サービスを提供することができます。もし学習教材は問題があれば、或いは試験に不合格になる場合は、全額返金することを保証いたします。Goldmile-InfobizのAmazonのMLS-C01真実試験試験トレーニング資料は正確性が高くて、カバー率も広い。 それで、不必要な損失を避けできます。ご客様はMLS-C01真実試験問題集を購入してから、勉強中で何の質問があると、行き届いたサービスを得られています。 Goldmile-Infobizの AmazonのMLS-C01真実試験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MLS-C01真実試験試験問題とほぼ同じです。
AWS Certified Specialty MLS-C01 ふさわしい方式を選ぶのは一番重要なのです。
Amazon MLS-C01 - AWS Certified Machine Learning - Specialty真実試験問題集の購買に何か質問があれば、我々の職員は皆様のお問い合わせを待っています。 現在あなたに提供するのは大切なAmazonのMLS-C01 復習問題集資料です。あなたの購入してから、我々はあなたにAmazonのMLS-C01 復習問題集資料の更新情況をつど提供します。
IT職員にとって、MLS-C01真実試験試験認定書はあなたの実力を証明できる重要なツールです。だから、Amazon MLS-C01真実試験試験に合格する必要があります。それで、弊社の質高いMLS-C01真実試験試験資料を薦めさせてください。
私たちより、Amazon MLS-C01真実試験試験を知る人はいません。
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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 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 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: 4
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: 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