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ISO/IEC 42001:2023Artificial Intelligence Management System Lead Auditor Exam

  • Exam Number/Code : ISO-IEC-42001-Lead-Auditor
  • Exam Name : ISO/IEC 42001:2023Artificial Intelligence Management System Lead Auditor Exam
  • Questions and Answers : 213 Q&As
  • Update Time: 2019-01-10
  • Price: $ 99.00 $ 39.00

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NEW QUESTION: 1
All of the following are examples of ways to generate options for mutual gain during negotiations EXCEPT______________
A. Multiplying options by shuttling between the specific and the general
B. Separating inventing from deciding
C. Options broadening
D. Zero-sum game analysis
Answer: D
Explanation:
Explanation/Reference:
Explanation:
Achieving mutual gain during negotiations means that each party benefits by the decisions made. A zero- sum game is where one side wins at the expense of the other.

NEW QUESTION: 2
In a Cisco ACI Multi-Site fabric, the Inter-Site BUM Traffic Allow option is enabled in a specific stretched bridge domain. What is used to forward BUM traffic to all endpoints in the same broadcast domain?
A. egress replication on the source leaf switches
B. egress replication on the destination leaf switches
C. ingress replication on the spines in the source site
D. ingress replication on the spines in the destination site
Answer: D
Explanation:
Reference:
http://yves-louis.com/DCI/?p=1277

NEW QUESTION: 3
You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training.
You must deploy the model to a context that allows for real-time GPU-based inferencing.
You need to configure compute resources for model inferencing.
Which compute type should you use?
A. Azure Container Instance
B. Azure Kubernetes Service
C. Machine Learning Compute
D. Field Programmable Gate Array
Answer: B
Explanation:
Explanation
You can use Azure Machine Learning to deploy a GPU-enabled model as a web service. Deploying a model on Azure Kubernetes Service (AKS) is one option. The AKS cluster provides a GPU resource that is used by the model for inference.
Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of CPUs offers performance advantages on highly parallelizable computation.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-inferencing-gpus