IBM Cloud Pak for Data v4.x Solution Architecture 온라인 연습
최종 업데이트 시간: 2024년11월08일
당신은 온라인 연습 문제를 통해 IBM C1000-136 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 C1000-136 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 63개의 시험 문제와 답을 포함하십시오.
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Question No : 1
Which statement describes the Analyze step in IBM's Al Ladder?
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Question No : 2
Several Db2 connections have been created on a Cloud Pak for Data platform.
What must be done to monitor the metrics of these databases?
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Question No : 3
Within Watson Knowledge Catalog, which two are data policies?
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Question No : 4
What Cloud Pak for Data service can be deployed with Cognos Analytics to leverage data governance policies?
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Question No : 5
Which statement is true about drift in OpenScale?
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Question No : 6
Which statement is true about data profiling?
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Question No : 7
Which two can be promoted to a deployment space?
정답: Explanation:
Promoting assets to a deployment space
The following assets can be promoted to a deployment space:
✑ Saved models
✑ Data assets for use in model deployments
✑ Data refinery flows and dependent assets
✑ Connections defined in your project
✑ Scripts
✑ Functions
✑ Shiny apps
https://www.ibm.com/docs/en/cloud-paks/cp-data/3.5.0?topic=functions-deployment-spaces
Question No : 8
Which statement describes the process to integrate Cloud Pak for Data with Guardium?
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Question No : 9
Which is required to connect a Cloud Pak for Data analytics project to an external Git repository?
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An analytics project requires a personal access token to connect to an external Git repository.
Question No : 10
Which Cloud Pak for Data service allows users to visualize data without writing any code?
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Question No : 11
Cognos Analytics integrates with which data science offering?
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Question No : 12
Which two types of custom workflows are supported by Watson Knowledge Catalog?
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Question No : 13
An energy company is creating an ML model to predict when there will be extra demand on the energy grid so preventative action can be taken to reduce usage leading up to the predicted event. The model is being monitored using OpenScale in Watson Studio.
Which action can be taken to ensure that they can identify when the model negatively affects certain locations based on the input data?