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| Topic |
Details |
| Topic 1 |
- 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 2 |
- 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 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.
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| Topic 4 |
- 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 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.
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Peoplecert DevOps Institute AIOps Foundation V1.0 Sample Questions (Q17-Q22):
NEW QUESTION # 17
Which of the following technologies is deterministic?
- A. Analytics
- B. Artificial Intelligence
- C. Machine Learning
- D. Neural networks
Answer: A
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 # 18
Which of these data comes from monitoring rather than application or infrastructure telemetry?
- A. Alerts
- B. Traces
- C. Metrics
- D. Logs
Answer: A
Explanation:
In IT operations, monitoring tools generate alerts to notify teams of significant events or anomalies that may require attention. These alerts are distinct from application or infrastructure telemetry data, such as metrics, logs, or traces, which provide detailed insights into system performance and behavior.
Alerts serve as a higher-level indication that something within the system deviates from the norm, prompting further investigation or action. In the AIOps Foundation course, the importance of effective alert management is emphasized to reduce noise and improve incident response.
In the context of IT operations and AIOps (Artificial Intelligence for IT Operations), it's essential to distinguish between different types of data sources:
* Metrics:These are numerical data points that represent the performance of systems over time. Metrics are typically collected from applications and infrastructure components to monitor aspects like CPU usage, memory consumption, and response times. They provide insights into the health and performance of the system.
* Logs:Logs are detailed, time-stamped records of events generated by applications, infrastructure, and other systems. They capture a wide range of information, including errors, warnings, and informational messages, which are crucial for troubleshooting and understanding system behavior.
* Alerts:Alerts are notifications generated by monitoring tools when specific conditions or thresholds are met. They are derived from the analysis of metrics, logs, and other telemetry data. Alerts serve as signals to IT operations teams that something requires attention.
* Traces:Traces track the flow of requests through various components of an application, providing visibility into the execution path and performance of distributed systems. They are essential for understanding the interactions between different services and identifying bottlenecks.
Among these,alertsare the data that come specifically from monitoring activities. Monitoring systems analyze metrics, logs, and traces to detect anomalies or threshold breaches and generate alerts accordingly. Therefore, alerts are a product of monitoring rather than raw telemetry data from applications or infrastructure.
This distinction is crucial in AIOps, where integrating and analyzing various data types enable proactive IT operations management. By understanding the origins and roles of metrics, logs, alerts, and traces, organizations can implement more effective monitoring strategies and leverage AIOps platforms to enhance system reliability and performance.
For a deeper understanding of these concepts, the DevOps Institute's AIOps Foundation course provides comprehensive coverage of data sources and types, as well as their roles in modern IT operations
NEW QUESTION # 19
Which definition BEST describes Big Data?
- A. Data sets thatlive in data warehouses or lakes
- B. Data sets of structured data that have grown over time
- C. Data sets that are so large they can only be interrogated using Al
- D. Data sets that are "too large" or diverse causing traditional data processing techniques to be ineffective
Answer: D
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 # 20
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. Increase in MTTA, Increase in MTTR and Decrease in MTBF
- C. Decrease in MTTA. Decrease in MTTR and Increase in MTBF
- D. Decrease in MTTA, Increase in MTTR and Decrease in MTBF
Answer: C
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 # 21
What is an effective way for an AlOps system to provide visibility?
- A. With a pub/sub architecture
- B. Using Slack or Teams
- C. Through dashboards and metrics
- D. Via email
Answer: C
NEW QUESTION # 22
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