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UiPath UiPath-SAIv1 Exam Objectives, UiPath-SAIv1 Customizable Exam Mode | Interactive UiPath-SAIv1 Course - Grad-Bsru

UiPath Certified Professional Specialized AI Professional v1.0

Questions and Answers : 430 Q&As

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Product Description Exam Number/Code: UiPath-SAIv1

Exam Number/Code: UiPath-SAIv1

"UiPath Certified Professional Specialized AI Professional v1.0", also known as UiPath-SAIv1 exam, is a UiPath Certification. With the complete collection of questions and answers, Grad-Bsru has assembled to take you through 430 Q&As to your UiPath-SAIv1 Exam preparation. In the UiPath-SAIv1 exam resources, you will cover every field and category in UiPath UiPath Certified Professional helping to ready you for your successful UiPath Certification.

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NEW QUESTION: 1
An Administrator would like to find out if a former employee has misused company data in Sales force prior to leaving the company. Which method should be used to verify the employee's activity in Sales force?
A. Setup audit trail
B. Create a customer report for users.
C. Download full login hi story
D. Log analysis
Answer: D

NEW QUESTION: 2
偽装された送信元アドレスを持つパケットを転送する代わりにドロップできる技術はどれですか?
A. ICMP到達不能メッセージ
B. ICUPリダイレクト
C. uRPF
D. TACACS +
E. SNMPv3
Answer: A

NEW QUESTION: 3
世界中にいる患者の医療記録の分析を実行する予定です。
クラウドでのデータの保存と処理を回避するソリューションを推奨する必要があります。
推奨事項に何を含めるべきですか?
A. コンテナーをサポートするテキスト分析API
B. Azure Machine Learning Studio
C. MMLSparkを使用するApache Sparkクラスター
D. Azure Machine Learningサービス
Answer: C
Explanation:
The Microsoft Machine Learning Library for Apache Spark (MMLSpark) assists in provisioning scalable machine learning models for large datasets, especially for building deep learning problems. MMLSpark works with SparkML pipelines, including Microsoft CNTK and the OpenCV library, which provide end-to-end support for the ingress and processing of image input data, categorization of images, and text analytics using pretrained deep learning algorithms.
References:
https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781789131956/10/ ch10lvl1sec61/an-overview-of-the-microsoft-machine-learning-library-for-apache-spark-mmlspark

NEW QUESTION: 4
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?
A. Create a tf.data.Dataset.prefetch transformation
B. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
C. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
D. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
Answer: D


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