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AIOps-Foundation시험패스가능한공부자료덤프의모든문제를기억하면시험패스가능
성공으로 향하는 길에는 많은 방법과 방식이 있습니다. Peoplecert인증 AIOps-Foundation시험을 패스하는 길에는ExamPassdump의Peoplecert인증 AIOps-Foundation덤프가 있습니다. ExamPassdump의Peoplecert인증 AIOps-Foundation덤프는 실제시험 출제방향에 초점을 두어 연구제작한 시험준비공부자료로서 높은 시험적중율과 시험패스율을 자랑합니다.국제적으로 승인해주는 IT자격증을 취득하시면 취직 혹은 승진이 쉬워집니다.
Peoplecert AIOps-Foundation 시험요강:
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AIOps-Foundation인증시험대비 공부문제 & AIOps-Foundation최신 시험 최신 덤프
경쟁율이 치열한 IT업계에서 아무런 목표없이 아무런 희망없이 무미건조한 생활을 하고 계시나요? 다른 사람들이 모두 취득하고 있는 자격증에 관심도 없는 분은 치열한 경쟁속에서 살아남기 어렵습니다. Peoplecert인증 AIOps-Foundation시험패스가 힘들다한들ExamPassdump덤프만 있으면 어려운 시험도 쉬워질수 밖에 없습니다. Peoplecert인증 AIOps-Foundation덤프에 있는 문제만 잘 이해하고 습득하신다면Peoplecert인증 AIOps-Foundation시험을 패스하여 자격증을 취득해 자신의 경쟁율을 업그레이드하여 경쟁시대에서 안전감을 보유할수 있습니다.
최신 PeopleCert DevOps AIOps-Foundation 무료샘플문제 (Q37-Q42):
질문 # 37
The effectiveness of an AlOps implementation is dependent on what performances?
- A. In-houseAl skills
- B. Number of data sources
- C. System complexity
- D. Machine Learning Model Performance
정답:D
설명:
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.
질문 # 38
Which algorithm Type is helpful in categorizing data in a supervised learning model?
- A. Classification
- B. Regression
- C. Clustering
- D. Association
정답:A
설명:
In supervised learning models,classification algorithmsare employed to categorize data into distinct classes or labels. These algorithms learn from a labeled dataset, where the input data is paired with the correct output, enabling the model to make accurate predictions on new, unseen data. For instance, classification can be used to determine whether an email is 'spam' or 'not spam'. This method is fundamental in various applications, including fraud detection, image recognition, and medical diagnosis. The DevOps Institute's AIOps Foundation course emphasizes the importance of classification in building predictive models that enhance IT operations.
질문 # 39
Which of the following is a characteristic of Machine Learning?
- A. A superset of Al
- B. Gradually improves accuracy through iterative optimization
- C. Requires explicit programming to learn
- D. Uses small amounts of historical data to generate accurate inferences or prediction
정답:B
설명:
Machine Learning (ML) involves algorithms that learn from data and improve their performance over time through iterative optimization. Unlike traditional programming, where explicit instructions are coded, ML models identify patterns and make predictions based on historical data, refining their accuracy as they process more information.
The AIOps Foundation course covers core technologies of machine learning, emphasizing how these models enhance IT operations by automating tasks and providing predictive insights.
질문 # 40
What does AlOps stand for?
- A. Artificial Intelligence in DevOps
- B. Augmented Interfaces in IT Operations
- C. Artificial Intelligence Operations
- D. Artificial Intelligence in IT Operations
정답:D
설명:
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.
질문 # 41
What is a big advantage of AlOps over ITOA?
- A. It can predict the future
- B. It works with large datasets
- C. It can understand the past
- D. It helps operations be reactive
정답:A
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 # 42
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