PROFESSIONAL PEOPLECERT - AIOPS-FOUNDATION - DEVOPS INSTITUTE AIOPS FOUNDATION V1.0 PRACTICE EXAMS FREE

Professional Peoplecert - AIOps-Foundation - DevOps Institute AIOps Foundation V1.0 Practice Exams Free

Professional Peoplecert - AIOps-Foundation - DevOps Institute AIOps Foundation V1.0 Practice Exams Free

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Tags: AIOps-Foundation Practice Exams Free, AIOps-Foundation Instant Discount, AIOps-Foundation Valid Test Tutorial, AIOps-Foundation Latest Braindumps Book, Valid AIOps-Foundation Study Notes

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Peoplecert AIOps-Foundation Exam Syllabus Topics:

TopicDetails
Topic 1
  • Core Technologies: Big Data: This section of the exam measures the skills of data engineers and covers an introduction to Big Data, including its definition, characteristics, and the Five V's (Volume, Velocity, Variety, Veracity, and Value). It also addresses various data sources and types relevant to AIOps. A key skill assessed is identifying different types of data utilized in AIOps environments.
Topic 2
  • AIOps and Operations Metrics: This section of the exam measures the skills of performance analysts and covers industry-standard metrics used to quantify the outcomes of implementing AIOps solutions.
Topic 3
  • Evaluating AIOps Impact: This section of the exam measures the skills of professionals and covers methods for measuring the effectiveness of AIOps deployments. It discusses how to assess potential benefits such as improved efficiency and reduced operational costs.
Topic 4
  • AIOps Use Cases and Organisational Mindset: This section of the exam measures the skills of the target audience and covers the challenges and opportunities associated with applying AIOps within organizations. It focuses on fostering an organizational mindset that embraces innovation through AIOps.
Topic 5
  • Core Technologies: Machine Learning (ML): This section of the exam measures the skills of machine learning practitioners and covers the role of AI and machine learning in AIOps. It includes discussions on supervised versus unsupervised learning, differences between ML and analytics, and training models for practical applications. A vital skill evaluated is understanding how to apply machine learning techniques to enhance operational efficiency.
Topic 6
  • Implementing AIOps: This section of the exam measures the skills of project managers and covers challenges, trends, and ethical considerations organizations may face when deploying an AIOps initiative. It emphasizes strategic planning for successful implementation while addressing potential risks.

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Peoplecert DevOps Institute AIOps Foundation V1.0 Sample Questions (Q22-Q27):

NEW QUESTION # 22
Which Al models can mimic human narrative?

  • A. AlOps
  • B. Big data
  • C. Generative
  • D. Large language

Answer: C

Explanation:
In the realm of artificial intelligence (AI), various models are designed to perform specific tasks.
Understanding these models is crucial, especially in the context of AIOps (Artificial Intelligence for IT Operations). Here's a breakdown of the options provided:
* Big Data: This term refers to the vast volumes of structured and unstructured data generated daily. Big Data itself is not an AI model but serves as a foundational element for AI and machine learning models, providing the necessary data for analysis and learning.
DevOps Institute
* Large Language Models: These are AI models trained on extensive text data to understand and generate human-like language. While they can produce coherent text, their primary function is to process and generate language based on input data. They are a subset of generative models but not the only type capable of mimicking human narratives.
* Generative Models: These AI models are designed to create new data instances that resemble a given dataset. In the context of language, generative models can produce human-like narratives, making them capable of mimicking human storytelling and conversation. Generative models encompass various architectures, including Generative Adversarial Networks (GANs) and certain types of Large Language Models.
* AIOps: This refers to the application of AI in IT operations to enhance and automate processes.
AIOps itself is not an AI model but a practice that leverages AI models, including generative models, to improve IT operations.
DevOps Institute
Therefore, the AI models that can mimic human narrative are Generative Models. These models are specifically designed to generate new, human-like content, making them suitable for tasks involving the creation of narratives.
For a more in-depth understanding of AI models and their applications in IT operations, the DevOps Institute's AIOps Foundation course provides comprehensive insights into how AI, including generative models, can be integrated into organizational frameworks to enhance IT operations.


NEW QUESTION # 23
What is an effective way for an AlOps system to provide visibility?

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

Answer: D


NEW QUESTION # 24
How did systems architecture transform?

  • A. From docker to OCI
  • B. From object oriented languages to functional languages
  • C. From cloud to edge
  • D. From monoliths to microservices

Answer: D

Explanation:
System architecture has evolved significantly, transitioning from monolithic structures to microservices.
* Monolithic Architecture: In this traditional model, applications are built as a single, unified unit. While simpler to develop initially, monoliths can become cumbersome to manage, scale, and update as they grow in complexity.
* Microservices Architecture: This modern approach decomposes applications into smaller, independent services that communicate through APIs. Each microservice handles a specific function, allowing for greater flexibility, scalability, and ease of deployment.


NEW QUESTION # 25
Which algorithm Type is helpful in categorizing data in a supervised learning model?

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

Answer: D

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 # 26
Which of the following technologies is deterministic?

  • A. Neural networks
  • B. Artificial Intelligence
  • C. Machine Learning
  • D. Analytics

Answer: D

Explanation:
Deterministic technologies operate with predictable outcomes based on specific inputs. Analytics is a deterministic process, as it involves the systematic analysis of data to produce consistent and repeatable results. Given the same data set and analytical methods, analytics will yield the same conclusions, making it a deterministic approach. In contrast, technologies like machine learning, artificial intelligence, and neural networks are probabilistic, as they involve learning from data and making inferences that may vary with different inputs or training processes.


NEW QUESTION # 27
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