TEST AIOPS-FOUNDATION FREE & AIOPS-FOUNDATION EXAM TRAINING

Test AIOps-Foundation Free & AIOps-Foundation Exam Training

Test AIOps-Foundation Free & AIOps-Foundation Exam Training

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Tags: Test AIOps-Foundation Free, AIOps-Foundation Exam Training, Exam AIOps-Foundation Answers, AIOps-Foundation Questions, AIOps-Foundation Certification Exam

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Peoplecert DevOps Institute AIOps Foundation V1.0 Sample Questions (Q31-Q36):

NEW QUESTION # 31
Reactive Operations rely on:

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

Answer: A

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 # 32
Which are indicators of potential value to AlOps implementation?

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

Answer: B

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 # 33
How should outcomes of an AlOps system be defined?

  • A. Loosely and randomly
  • B. Realistically and aimed at gradual improvement
  • C. AlOps is a silver bullet that will increase resiliency overnight
  • D. Not-deterministically

Answer: B

Explanation:
Defining outcomes for an AIOps system should be approachedrealistically, with a focus ongradual improvement. AIOps is not a quick fix; it requires careful planning, realistic goal-setting, and iterative enhancements. By setting achievable objectives and continuously refining processes, organizations can effectively integrate AIOps into their IT operations, leading to sustained improvements over time.


NEW QUESTION # 34
What is Step 1 in the AlOps Capability Scale?

  • A. Add automation for self-healing
  • B. Automate toils using Al Insights
  • C. Reduce MTTR through noise reduction
  • D. Use chaos engineering for antifragility

Answer: C

Explanation:
In the AIOps Capability Scale,Step 1focuses on reducing Mean Time to Repair (MTTR) by minimizing alert noise. This initial phase involves implementing AIOps solutions to filter and correlate alerts, thereby decreasing the volume of irrelevant notifications. By reducing noise, IT teams can concentrate on critical issues, leading to faster incident resolution and improved system reliability. This foundational step sets the stage for more advanced AIOps capabilities.


NEW QUESTION # 35
Which definition BEST describes Big Data?

  • A. Data sets that are so large they can only be interrogated using Al
  • B. Data sets that are "too large" or diverse causing traditional data processing techniques to be ineffective
  • C. Data sets thatlive in data warehouses or lakes
  • D. Data sets of structured data that have grown over time

Answer: B

Explanation:
Big Data refers to data sets that are so large, fast, or complex that traditional data processing methods are inadequate to handle them. This concept is characterized by the Five V's:
* Volume: The sheer amount of data generated.
* Velocity: The speed at which new data is produced and needs to be processed.
* Variety: The different types of data (structured, unstructured, semi-structured).
* Veracity: The quality and accuracy of the data.
* Value: The usefulness of the data for decision-making.
In the context of AIOps, understanding Big Data is crucial as it involves combining big data analytics with machine learning algorithms to enhance IT operations.


NEW QUESTION # 36
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