AIOps-Foundation유효한덤프문제, AIOps-Foundation자격증문제

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Peoplecert AIOps-Foundation 시험요강:

주제	소개
주제 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.
주제 2	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.
주제 3	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.
주제 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.
주제 5	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.

>> AIOps-Foundation유효한 덤프문제 <<

AIOps-Foundation자격증문제 & AIOps-Foundation인증공부문제

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최신 PeopleCert DevOps AIOps-Foundation 무료샘플문제 (Q38-Q43):

질문 # 38
The incident related metric MTTD means:

A. Mean time to Detect
B. Mean Time to Deployment
C. Mean Time to Distribution
D. Mean Time to Delivery

정답：A

설명：
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.

질문 # 39
Reactive Operations rely on:

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

정답：D

설명：
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.

질문 # 40
Which of the following technologies is deterministic?

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

정답：D

설명：
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.

질문 # 41
Surfacing relevant notifications and alerts from among large volumes of alerts is satisfied by this use case:

A. Event correlation
B. Root cause analysis
C. Anomaly detection
D. Alert noise reduction

정답：D

설명：
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.

질문 # 42
What is a big advantage of AlOps over ITOA?

A. It works with large datasets
B. It can understand the past
C. It can predict the future
D. It helps operations be reactive

정답：C

설명：
A significant advantage ofAIOps (Artificial Intelligence for IT Operations)over traditionalIT Operations Analytics (ITOA)is its ability topredict future events. While ITOA focuses on analyzing historical data to understand past incidents, AIOps leverages advanced machine learning algorithms to forecast potential issues before they occur. This predictive capability enables proactive problem resolution, reducing downtime and improving system reliability. The DevOps Institute's AIOps Foundation course highlights this forward- looking approach as a key benefit of implementing AIOps in modern IT environments.

질문 # 43
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