A financial services company is implementing an AIOps platform to reduce Mean Time to Resolution (MTTR) for critical trading applications. The platform ingests logs, metrics, and traces from a hybrid cloud environment. The operations team is struggling with a high volume of false positive alerts from the new anomaly detection module. Which initial action is the most critical for improving the signal-to-noise ratio?
A large retail organization is planning its AIOps implementation strategy. The goal is to demonstrate value quickly to secure further funding. The project lead has identified several potential pilot projects. According to AIOps implementation best practices, which TWO of the following projects are most suitable for an initial pilot? (Select TWO)
True or False: AIOps requires all operational data, such as logs, metrics, and traces, to be converted into a single, structured format before it can be processed by machine learning algorithms.
**Case Study:** **Company Background:** Global Logistics Inc. (GLI) operates a massive, distributed logistics network supported by a complex mix of legacy on-premises systems and modern cloud-native microservices. Their IT Operations team is overwhelmed by the sheer volume and complexity of operational data, leading to frequent service degradations that impact package tracking and delivery schedules. The executive team has approved a strategic initiative to adopt AIOps to improve operational stability and efficiency. **Current Situation:** The IT Operations team uses over a dozen disparate monitoring tools, each with its own alerting system. There is no central data repository, and engineers spend hours manually correlating alerts across different dashboards during an incident. The Site Reliability Engineering (SRE) team has established Service Level Objectives (SLOs), but they are frequently breached due to slow incident response. Data is siloed in different business units, and there is significant cultural resistance to sharing data and adopting new, automated processes. **Requirements & Constraints:** 1. The AIOps solution must be implemented in phases, starting with a pilot project that shows a clear Return on Investment (ROI) within six months. 2. The solution must reduce Mean Time to Identify (MTTI) by at least 50% for critical incidents. 3. The implementation must address the cultural resistance by demonstrating tangible benefits to the operations teams without initially threatening their job roles. 4. The solution must be able to process data from both the on-premises mainframes (generating EBCDIC logs) and the Kubernetes-based cloud environment (generating JSON logs and Prometheus metrics). Which of the following represents the most effective strategic approach for GLI's AIOps implementation?
An SRE team is using an AIOps platform to monitor a microservices-based application. The team wants to measure the direct impact of AIOps on operational efficiency. Which metric provides the most direct and quantifiable measure of improvement in the team's incident investigation process?
A DevOps team is adopting AIOps to enhance their CI/CD pipeline. One of the goals is to automatically halt a problematic deployment before it impacts a significant number of users. Which AIOps use case is most relevant to achieving this goal?
When discussing the core technologies behind AIOps, what is the primary role of 'Big Data'?
An organization is evaluating the business impact of its AIOps investment. Which of the following is considered a business-level metric, as opposed to a purely operational metric?
A key cultural challenge in adopting AIOps is the 'fear of the black box,' where operations staff distrusts the recommendations made by the AI. What is the most effective strategy to foster trust and encourage adoption?
The evolution from traditional IT monitoring to AIOps can be seen as a progression of capabilities. Which option correctly orders this evolution from least to most mature? 1. **Observe**: Aggregating data into a single view. 2. **Engage**: Automating responses and remediation actions. 3. **Act**: Providing context and inferring causality for decision support.