2025 AIOPS-FOUNDATION KEY CONCEPTS FREE PDF | HIGH PASS-RATE AIOPS-FOUNDATION VALID TORRENT: DEVOPS INSTITUTE AIOPS FOUNDATION V1.0

2025 AIOps-Foundation Key Concepts Free PDF | High Pass-Rate AIOps-Foundation Valid Torrent: DevOps Institute AIOps Foundation V1.0

2025 AIOps-Foundation Key Concepts Free PDF | High Pass-Rate AIOps-Foundation Valid Torrent: DevOps Institute AIOps Foundation V1.0

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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
  • 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 3
  • 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 4
  • 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 5
  • 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 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 (Q12-Q17):

NEW QUESTION # 12
How did systems architecture transform?

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

Answer: B

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 # 13
At which stage does the data pipeline deduplicate data?

  • A. Storage
  • B. Enrichment/filtering
  • C. Cleaning/integration
  • D. Extraction/collection

Answer: C

Explanation:
In a data pipeline, deduplication occurs during the cleaning and integration stage. This process involves identifying and removing duplicate records to ensure data quality and accuracy. By eliminating redundancies, organizations can maintain a single source of truth, leading to morereliable analytics and decision-making.
The DevOps Institute's AIOps Foundation course underscores the importance of data cleaning and integration in preparing data for effective analysis and operational use.


NEW QUESTION # 14
Which are potential outcomes of an AlOps implementation?

  • A. All of the above
  • B. Continuously improve its value to the organization
  • C. Reduce cost and eliminate waste
  • D. Simplify operations

Answer: A

Explanation:
Potential outcomes of AIOps implementation include:
* Simplifying operationsby automating routine tasks and reducing manual intervention.
* Reducing costs and eliminating wastethrough better resource utilization and operational efficiency.
* Continuous improvementof its value to the organization by enabling proactive, data-driven decisions.
The DevOps Institute emphasizes these benefits as the primary drivers for adopting AIOps in IT operations.


NEW QUESTION # 15
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 # 16
Which are indicators of potential value to AlOps implementation?

  • A. All of the above
  • B. Poor alert quality with lots of noise
  • C. Difficulty in triaging and root cause analysis
  • D. Upward trending number of alerts

Answer: A

Explanation:
Indicators that suggest potential value in implementing AIOps include:
* Upward Trending Number of Alerts: An increasing volume of alerts can overwhelm IT teams, leading to alert fatigue and missed critical issues.
* Poor Alert Quality with Lots of Noise: High levels of false positives or irrelevant alerts can obscure genuine problems, reducing operational efficiency.
* Difficulty in Triaging and Root Cause Analysis: Challenges in quickly identifying and resolving the underlying causes of incidents can prolong downtime and impact service quality.
Implementing AIOps addresses these challenges by utilizing artificial intelligence and machine learning to enhance alert management, reduce noise, and streamline incident resolution, as outlined in the DevOps Institute's AIOps Foundation.


NEW QUESTION # 17
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