NEW AIOPS-FOUNDATION LEARNING MATERIALS, RELIABLE AIOPS-FOUNDATION EXAM PDF

New AIOps-Foundation Learning Materials, Reliable AIOps-Foundation Exam Pdf

New AIOps-Foundation Learning Materials, Reliable AIOps-Foundation Exam Pdf

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Peoplecert DevOps Institute AIOps Foundation V1.0 Sample Questions (Q26-Q31):

NEW QUESTION # 26
What is the meaning of Digital Transformation?

  • A. Adoption of digital technologies for accelerated Innovation and improved customer experience
  • B. Refactoring all software to a newer technology stack
  • C. Replacing all human operators with artificial Intelligence
  • D. Replacing all analog systems with digital equivalents

Answer: A

Explanation:
Digital Transformation refers to the strategic adoption of digital technologies to fundamentally change how organizations operate, deliver value to customers, and foster innovation.
It is not about simply replacing analog systems or eliminating human operators but integrating technology to improve efficiency, decision-making, and customer satisfaction.
DevOps Institute defines it as leveraging tools, automation, and cultural shifts to enable faster and more effective innovation cycles.
References highlight improved agility, scalability, and customer-focused outcomes as key objectives of Digital Transformation.


NEW QUESTION # 27
With AlOps, offering aggressive SLAs results in:

  • A. There is no relation
  • B. Decreased risk
  • C. Increased risk
  • D. No change to risk

Answer: C

Explanation:
Offering aggressive Service Level Agreements (SLAs) with AIOps can lead to increased risk if the organization lacks the necessary infrastructure and processes to meet these stringent targets. Unrealistic SLAs may result in overcommitment, leading to potential service breaches, customer dissatisfaction, and reputational damage. It's essential to set achievable SLAs that align with the organization's capabilities, even when leveraging advanced tools like AIOps.


NEW QUESTION # 28
Which algorithm Type is helpful in categorizing data in a supervised learning model?

  • A. Association
  • B. Classification
  • C. Clustering
  • D. Regression

Answer: B

Explanation:
In supervised learning models,classification algorithmsare employed to categorize data into distinct classes or labels. These algorithms learn from a labeled dataset, where the input data is paired with the correct output, enabling the model to make accurate predictions on new, unseen data. For instance, classification can be used to determine whether an email is 'spam' or 'not spam'. This method is fundamental in various applications, including fraud detection, image recognition, and medical diagnosis. The DevOps Institute's AIOps Foundation course emphasizes the importance of classification in building predictive models that enhance IT operations.


NEW QUESTION # 29
What is an effective way for an AlOps system to provide visibility?

  • A. Through dashboards and metrics
  • B. With a pub/sub architecture
  • C. Using Slack or Teams
  • D. Via email

Answer: A

Explanation:
An effective AIOps system provides visibility into IT operations through comprehensive dashboards and metrics. These tools offer real-time insights into system performance, health, and anomalies, enabling IT teams to monitor operations proactively. Dashboards consolidate data from various sources, presenting it in an accessible format, while metrics track key performance indicators essential for informed decision-making.


NEW QUESTION # 30
Which of the following describes MLOps?

  • A. Software that thinks like a human through analysis and reasoning to perform complex tasks
  • B. A set of capabilities that primarily focuses on the governance and the full life cycle management of all Al and decision models
  • C. Applying artificial intelligence to IT Operations
  • D. Implementing CI/CD. testing and accelerated development lifecycle to the machine learning model development

Answer: D

Explanation:
MLOps, or Machine Learning Operations, applies DevOps principles such as Continuous Integration and Continuous Deployment (CI/CD) to the development and deployment of machine learning models. This approach emphasizes automation, testing, and streamlined workflows to accelerate the machine learning lifecycle, ensuring models are reliable, reproducible, and maintainable in production environments.
The AIOps Foundation course discusses the relationship between AIOps and MLOps, highlighting how integrating these practices can enhance IT operations.


NEW QUESTION # 31
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