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Peoplecert - AIOps-Foundation–Newest Practice Test

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Peoplecert AIOps-Foundation Exam Syllabus Topics:

Topic
Details

Topic 1

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.

Topic 2

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 3

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 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.

Topic 5

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 6

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 7

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.

Peoplecert DevOps Institute AIOps Foundation V1.0 Sample Questions (Q38-Q43):

NEW QUESTION # 38
Which are potential outcomes of an AlOps implementation?

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

Answer: B

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 # 39
Which definition BEST describes Big Data?

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

Answer: A

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 # 40
Which of the following technologies is deterministic?

A. Machine Learning
B. Neural networks
C. Analytics
D. Artificial Intelligence

Answer: C

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 # 41
Targets of acceptable performance are defined in;

A. SLIs
B. SLAs
C. SLOs
D. KPIs

Answer: C

Explanation:
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.

NEW QUESTION # 42
Which are core technologies used by an AlOps system?

A. All of the above
B. Machine Learning
C. Automation
D. Big Data

Answer: A

Explanation:
AIOps systems rely on a combination of technologies to achieve their goals:
* Big Data: Collects and processes vast amounts of operational data from diverse sources to ensure comprehensive analysis.
* Machine Learning (ML): Identifies patterns, anomalies, and trends within the data, enabling predictive capabilities.
* Automation: Ensures rapid and reliable responses to identified issues, enhancing efficiency and minimizing manual intervention.
The interplay of these technologies creates a cohesive system capable of dynamic, scalable, and intelligent IT operations.
As per DevOps Institute, the integration of these technologies underpins the core functionality and effectiveness of AIOps solutions.

NEW QUESTION # 43
......

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