2025年最新のAIOps-Foundation試験問題集で最近更新された42問題
Peoplecert AIOps-Foundationリアル2025年最新のブレーン問題集で模擬試験問題集
Peoplecert AIOps-Foundation 認定試験の出題範囲：
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質問 # 24
Which of the MELT data types is specific to a microservices based system?
- A. Traces
- B. Logs
- C. Metrics
- D. Events
正解：A
解説：
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.
質問 # 25
The incident related metric MTTD means:
- A. Mean Time to Deployment
- B. Mean Time to Distribution
- C. Mean Time to Delivery
- D. Mean time to Detect
正解：D
解説：
Mean Time to Detect (MTTD)is an incident management metric that measures the average time taken to identify an issue within a system. A lower MTTD indicates a more responsive monitoring system, allowing for quicker remediation and minimizing potential impact. Improving MTTD is crucial for maintaining system reliability and performance. The DevOps Institute's AIOps Foundation course emphasizes the importance of MTTD in evaluating the effectiveness of IT operations and the implementation of AIOps solutions to enhance detection capabilities.
質問 # 26
Discovering unexpected changes in system behavior or performance is satisfied by this use case:
- A. Root cause analysis
- B. Alert noise reduction
- C. Anomaly detection
- D. Event correlation
正解：C
解説：
Anomaly detectionrefers to identifying unexpected changes or deviations in system behavior or performance.
This use case is essential for proactively detecting issues that may not have predefined patterns or signatures, enabling faster incident resolution.
The DevOps Institute's AIOps Foundation materials describe anomaly detection as a key feature of AIOps platforms to enhance monitoring capabilities.
質問 # 27
How should outcomes of an AlOps system be defined?
- A. Not-deterministically
- B. AlOps is a silver bullet that will increase resiliency overnight
- C. Realistically and aimed at gradual improvement
- D. Loosely and randomly
正解：C
解説：
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.
質問 # 28
What is the meaning of Digital Transformation?
- A. Replacing all human operators with artificial Intelligence
- B. Refactoring all software to a newer technology stack
- C. Replacing all analog systems with digital equivalents
- D. Adoption of digital technologies for accelerated Innovation and improved customer experience
正解：D
解説：
Digital Transformation refers to the strategic adoption of digital technologies to fundamentally change how organizations operate, deliver value to customers, and foster innovation.
It is not about simply replacing analog systems or eliminating human operators but integrating technology to improve efficiency, decision-making, and customer satisfaction.
DevOps Institute defines it as leveraging tools, automation, and cultural shifts to enable faster and more effective innovation cycles.
References highlight improved agility, scalability, and customer-focused outcomes as key objectives of Digital Transformation.
質問 # 29
How do SLAs relate To AlOps?
- A. There is no relationship between AlOps and SLAs
- B. AlOps automates the generation of SLA documentation
- C. AlOps reduces the risk and improves SLA targets by overall improving IT Operations speed and capabilities.
- D. AlOps indicates which SLOs to define in an SLA
正解：C
解説：
Service Level Agreements (SLAs) define the expected performance and availability standards for IT services.
AIOps enhances the ability to meet and exceed these SLA targets by improving IT operations' speed and capabilities. Through the integration of big data analytics and machine learning, AIOps enables real-time monitoring, rapid issue detection, and automated responses, reducing downtime and enhancing service reliability. This proactive approach minimizes risks associated with SLA breaches and ensures that IT services consistently meet agreed-upon performance standards.
質問 # 30
Targets of acceptable performance are defined in;
- A. SLIs
- B. KPIs
- C. SLOs
- D. SLAs
正解：C
解説：
Service Level Objectives (SLOs)define specific, measurable targets for acceptable performance of a service.
They are critical components ofService Level Agreements (SLAs), providing clear benchmarks for service reliability and availability. By setting SLOs, organizations can align operational performance with business goals and customer expectations. The DevOps Institute's AIOps Foundation course outlines how establishing and monitoring SLOs is essential for effective service management and how AIOps can assist in meeting these objectives through enhanced monitoring and predictive analytics.
質問 # 31
With AlOps, offering aggressive SLAs results in:
- A. Decreased risk
- B. Increased risk
- C. No change to risk
- D. There is no relation
正解：B
解説：
Offering aggressive Service Level Agreements (SLAs) with AIOps can lead to increased risk if the organization lacks the necessary infrastructure and processes to meet these stringent targets. Unrealistic SLAs may result in overcommitment, leading to potential service breaches, customer dissatisfaction, and reputational damage. It's essential to set achievable SLAs that align with the organization's capabilities, even when leveraging advanced tools like AIOps.
質問 # 32
The various key areas in a system work together in the following loop:
- A. Audit, document and restore
- B. Observe, automate, act
- C. Observe, engage, act
- D. Automate, iterate and fail fast
正解：C
解説：
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.
質問 # 33
Which of the following technologies is deterministic?
- A. Machine Learning
- B. Analytics
- C. Neural networks
- D. Artificial Intelligence
正解：B
解説：
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.
質問 # 34
At which stage does the data pipeline deduplicate data?
- A. Storage
- B. Cleaning/integration
- C. Extraction/collection
- D. Enrichment/filtering
正解：B
解説：
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.
質問 # 35
Systems operation became elastic and dynamic thanks to:
- A. Machine Learning
- B. Adoption of thecloud
- C. Contamenzation
- D. Linux
正解：B
解説：
The adoption of cloud computing has transformed system operations, making them more elastic and dynamic.
Cloud platforms provide on-demand resource allocation, enabling systems to scale up or down based on workload requirements. This elasticity allows organizations to efficiently manage resources, reduce costs, and respond swiftly to changing demands. The dynamic nature of cloud services supports continuous integration and deployment, enhancing operational agility. The DevOps Institute's AIOps Foundation course emphasizes the significance of cloud adoption in modernizing IT operations and achieving operational excellence.
質問 # 36
What does reliability mean?
- A. The ability to keep a functioning state
- B. The ability to perform all desired functions
- C. The ability to be timely and easily maintained
- D. The ability to not create harm
正解：A
解説：
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.
質問 # 37
What does AlOps stand for?
- A. Artificial Intelligence in DevOps
- B. Artificial Intelligence in IT Operations
- C. Augmented Interfaces in IT Operations
- D. Artificial Intelligence Operations
正解：B
解説：
AIOps stands for "Artificial Intelligence in IT Operations." This term refers to the application of artificial intelligence (AI) and machine learning (ML) technologies to enhance and automate various aspects of IT operations. By leveraging big data analytics, AIOps platforms can analyze vast amounts of data generated by IT systems to identify patterns, detect anomalies, and automate responses to operational issues.
The DevOps Institute's AIOps Foundation course emphasizes that AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination. This integration enables IT teams to proactively manage complex IT environments, improve system performance, and reduce downtime.
Implementing AIOps involves several key steps:
* Data Aggregation: Collecting and aggregating data from various IT operations sources, such as logs, metrics, and events.
* Data Analysis: Applying machine learning algorithms to analyze the aggregated data, identifying patterns and anomalies that could indicate potential issues.
* Automated Response: Utilizing AI-driven insights to automate responses to detected issues, such as triggering alerts, executing remediation scripts, or adjusting system configurations.
* Continuous Improvement: Regularly refining AI models and operational processes based on feedback and evolving data patterns to enhance the effectiveness of the AIOps solution.
By following these steps, organizations can achieve a more proactive and efficient IT operations management approach, leading to improved reliability and performance of their IT services.
For more detailed information, refer to the DevOps Institute's AIOps Foundation course materials.
質問 # 38
What can one use to track system status if applying SRE principles?
- A. Number of alerts
- B. Number of commits to a repo
- C. User defect reports
- D. Error budgets
正解：D
解説：
In Site Reliability Engineering (SRE), error budgets are a key metric for tracking system status and balancing reliability with the pace of innovation.
* Error Budgets: An error budget quantifies the acceptable level of system unreliability over a specific period. It represents the permissible amount of downtime or failures, allowing teams to make informed decisions about deploying new features versus focusing on system stability.
By monitoring error budgets, organizations can effectively manage trade-offs between releasing new functionalities and maintaining system reliability, a practice supported by the DevOps Institute's AIOps Foundation principles.
質問 # 39
Surfacing relevant notifications and alerts from among large volumes of alerts is satisfied by this use case:
- A. Anomaly detection
- B. Root cause analysis
- C. Alert noise reduction
- D. Event correlation
正解：C
解説：
Alert noise reductionis the use case focused on identifying and prioritizing relevant alerts from a large volume of notifications, which helps prevent alert fatigue for IT teams.
By reducing noise, AIOps enables teams to focus on significant issues that require immediate attention, improving operational efficiency.
The DevOps Institute's AIOps Foundation course highlights this capability as a core advantage of AIOps systems.
質問 # 40
How did systems architecture transform?
- A. From object oriented languages to functional languages
- B. From cloud to edge
- C. From monoliths to microservices
- D. From docker to OCI
正解：C
解説：
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.
質問 # 41
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