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| 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.
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| Topic 2 |
- 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.
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| Topic 3 |
- 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.
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| Topic 4 |
- AIOps Fundamentals: This section of the exam measures the skills of IT operations professionals and covers the evolution of AIOps, differentiating it from IT Operations Analytics. It also explores the current stages of an AIOps system and its significance in modern IT environments. A key skill assessed is understanding the foundational concepts that drive AIOps adoption.
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| 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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| Topic 6 |
- 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.
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| Topic 7 |
- 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.
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Peoplecert DevOps Institute AIOps Foundation V1.0 Sample Questions (Q19-Q24):
NEW QUESTION # 19
Which of the MELT data types is specific to a microservices based system?
- A. Metrics
- B. Logs
- C. Traces
- D. Events
Answer: C
Explanation:
In microservices-based systems, "Traces" are a specific MELT (Metrics, Events, Logs, Traces) data type.
Traces track the flow of requests through various services, providing visibility into the interactions and performance of microservices. This tracing is crucial for diagnosing issues, understanding system behavior, and optimizing performance in complex, distributed environments. The DevOps Institute's AIOps Foundation course emphasizes the role of traces in observability practices, enabling teams to monitor and improve microservices architectures effectively.
For more detailed information, refer to the DevOps Institute's AIOps Foundation course materials.
NEW QUESTION # 20
The various key areas in a system work together in the following loop:
- A. Audit, document and restore
- B. Observe, engage, act
- C. Observe, automate, act
- D. Automate, iterate and fail fast
Answer: B
Explanation:
In the context of AIOps, the system operates through a continuous loop comprising three key stages:
* Observe: This initial phase involves monitoring and collecting data from various IT environments. By gathering metrics, logs, and events, the system gains visibility into its operations, enabling the detection of anomalies or performance issues.
* Engage: Once data is collected, this stage focuses on analyzing and correlating the information to identify patterns or issues. Engagement involves applying machine learning algorithms and analytics to interpret the observed data, facilitating informed decision-making.
* Act: Based on the insights derived from the engagement phase, the system takes appropriate actions to resolve identified issues or optimize performance. This may include automated responses such as scaling resources, restarting services, or alerting IT personnel for further investigation.
This cyclical process ensures that IT operations are continuously monitored, analyzed, and improved, aligning with the principles outlined in the DevOps Institute's AIOps Foundation.
NEW QUESTION # 21
The effectiveness of an AlOps implementation is dependent on what performances?
- A. System complexity
- B. Machine Learning Model Performance
- C. Number of data sources
- D. In-houseAl skills
Answer: B
Explanation:
The effectiveness of an AIOps implementation is significantly influenced by the performance of its machine learning (ML) models. These models are central to analyzing vast amounts of data, identifying patterns, and making predictions that enhance IT operations. Key factors include:
* Accuracy: The precision of the ML model in identifying and predicting issues directly impacts the reliability of the AIOps system.
* Training Data Quality: High-quality, relevant data is essential for training effective ML models.
* Model Adaptability: The ability of the model to adapt to new data and evolving system behaviors ensures sustained effectiveness.
Therefore, optimizing machine learning model performance is crucial for a successful AIOps deployment.
NEW QUESTION # 22
Which are potential outcomes of an AlOps implementation?
- A. Simplify operations
- B. Continuously improve its value to the organization
- C. Reduce cost and eliminate waste
- D. All of the above
Answer: D
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 # 23
What is Step 1 in the AlOps Capability Scale?
- A. Add automation for self-healing
- B. Automate toils using Al Insights
- C. Use chaos engineering for antifragility
- D. Reduce MTTR through noise reduction
Answer: D
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 # 24
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