プロなIT技術専門家になりたいのですか。速くAmazonのMLS-C01日本語版問題解説認定試験「AWS Certified Machine Learning - Specialty」を申し込みましょう。この認証がどんなに重要するかあなたもよく知っています。 この時代にはIT資格認証を取得するは重要になります。それでは、MLS-C01日本語版問題解説試験に参加しよう人々は弊社Goldmile-InfobizのMLS-C01日本語版問題解説問題集を選らんで勉強して、一発合格して、AmazonIT資格証明書を受け取れます。 Goldmile-Infobizのトレーニング資料は受験生が一番ほしい唯一なトレーニング資料です。
AWS Certified Specialty MLS-C01 あなたもきっとそう思うでしょう。
Amazon MLS-C01 - AWS Certified Machine Learning - Specialty日本語版問題解説資格試験に参加する意向があれば、当社のGoldmile-Infobizから自分に相応しい受験対策解説集を選らんで、認定試験の学習教材として勉強します。 Goldmile-Infobizの参考資料は時間の試練に耐えることができます。Goldmile-Infobizは現在の実績を持っているのは受験生の皆さんによって実践を通して得られた結果です。
今日は、試験の結果をチエックし、嬉しいことに、MLS-C01日本語版問題解説試験に合格しました。MLS-C01日本語版問題解説復習教材は有効的な資料です。MLS-C01日本語版問題解説試験は難しいです。
Amazon MLS-C01日本語版問題解説 - 皆さんからいろいろな好評をもらいました。
ローマは一日に建てられませんでした。多くの人にとって、短い時間でMLS-C01日本語版問題解説試験に合格できることは難しいです。しかし、幸いにして、MLS-C01日本語版問題解説の練習問題の専門会社として、弊社の最も正確な質問と回答を含むMLS-C01日本語版問題解説試験の資料は、MLS-C01日本語版問題解説試験対する問題を効果的に解決できます。MLS-C01日本語版問題解説練習問題をちゃんと覚えると、MLS-C01日本語版問題解説に合格できます。あなたはMLS-C01日本語版問題解説練習問題を選ばれば、試験に合格できますよ！
Goldmile-InfobizのAmazonのMLS-C01日本語版問題解説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。あなたがAmazonのMLS-C01日本語版問題解説認定試験に合格するのに最も良くて、最も必要な学習教材です。
MLS-C01 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 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 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: 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
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Updated: May 28, 2022