PASS GUARANTEED QUIZ 2025 AIOPS-FOUNDATION: HIGH HIT-RATE DEVOPS INSTITUTE AIOPS FOUNDATION V1.0 RELIABLE BRAINDUMPS PPT

Pass Guaranteed Quiz 2025 AIOps-Foundation: High Hit-Rate DevOps Institute AIOps Foundation V1.0 Reliable Braindumps Ppt

Pass Guaranteed Quiz 2025 AIOps-Foundation: High Hit-Rate DevOps Institute AIOps Foundation V1.0 Reliable Braindumps Ppt

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There are many users that are using DevOps Institute AIOps Foundation V1.0 (AIOps-Foundation) exam questions and rated it as one of the best in the market. The customers are pleased with DevOps Institute AIOps Foundation V1.0 (AIOps-Foundation) exam questions and all of them have passed the DevOps Institute AIOps Foundation V1.0 (AIOps-Foundation) certification exam on the very first try.

Peoplecert AIOps-Foundation Exam Syllabus Topics:

TopicDetails
Topic 1
  • AIOps in the Organisation: This section of the exam measures the skills of organizational leaders and covers how AIOps can be integrated into existing frameworks. It discusses the impact of AIOps on DevOps practices, site reliability, security measures, and managing system complexity. A critical skill evaluated is recognizing the organizational changes required for successful AIOps implementation.
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
  • 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 5
  • 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 (Q20-Q25):

NEW QUESTION # 20
What does reliability mean?

  • A. The ability to be timely and easily maintained
  • B. The ability to not create harm
  • C. The ability to keep a functioning state
  • D. The ability to perform all desired functions

Answer: C

Explanation:
Reliability in IT operations refers to a system's ability to consistently perform its intended functions without failure. This involves maintaining a functioning state over time, ensuring that services are available and operating correctly as expected. In the context of AIOps, enhancing reliability is a key objective, achieved through proactive monitoring, predictive analytics, and automated remediation. By leveraging AIOps, organizations can detect potential issues before they impact users, thereby maintaining system reliability and improving overall service quality.


NEW QUESTION # 21
Reactive Operations rely on:

  • A. Big Data
  • B. Prediction and inference
  • C. Leading indicators
  • D. Lagging indicators

Answer: D

Explanation:
Reactive operations focus on responding to incidents after they have occurred, relying on lagging indicators- metrics that reflect past events or performance. These indicators, such as system downtime reports or post- incident analyses, provide insights into issues that have already impacted the system. While useful for understanding and addressing past problems, reliance solely on lagging indicators can lead to delayed responses and prolonged downtime. AIOps aims to shift operations from reactive to proactive by utilizing leading indicators and predictive analytics to anticipate and prevent issues before they occur.


NEW QUESTION # 22
What is an effective way for an AlOps system to provide visibility?

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

Answer: B


NEW QUESTION # 23
What impact on incident related metrics is the desired outcome from AlOps implementation?

  • A. Decrease in MTTA. Decrease in MTTR and Decrease in MTBF
  • B. Decrease in MTTA. Decrease in MTTR and Increase in MTBF
  • C. Decrease in MTTA, Increase in MTTR and Decrease in MTBF
  • D. Increase in MTTA, Increase in MTTR and Decrease in MTBF

Answer: B

Explanation:
AIOps aims to improve incident-related metrics by:
* Decreasing Mean Time to Acknowledge (MTTA): Faster detection and acknowledgment of issues.
* Decreasing Mean Time to Resolve (MTTR): Quicker resolution through automation and actionable insights.
* Increasing Mean Time Between Failures (MTBF): Enhanced system reliability and reduced frequency of failures.
These improvements lead to more reliable IT operations, as highlighted in the DevOps Institute's AIOps Foundation course.


NEW QUESTION # 24
Which Al models can mimic human narrative?

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

Answer: D

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 # 25
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