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AIOps Foundation practice questions

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Question 1Origins and history of AIOps

The term AIOps was introduced by Gartner to describe a category of platforms that combine big data and machine learning to do what for IT operations?

  1. Standardize the physical layout of data center server racks
  2. Replace all IT staff with autonomous robots within a single quarter
  3. Provide accounting software for cloud billing reconciliation
  4. Automate and augment operational decision-making across monitoring and event data Correct answer.

Explanation

Gartner coined AIOps to describe platforms that apply analytics and machine learning to large volumes of operational data, aiming to automate and augment decision-making in IT operations. The emphasis is on augmenting human operators with insight, not eliminating staff or addressing physical infrastructure or finance.

Question 2Origins and history of AIOps

Which pairing best reflects the two data sources that early AIOps platforms were designed to unify?

  1. Marketing CRM records and payroll databases
  2. Spreadsheet budgets and human resources records
  3. Source code repositories and design mockups
  4. IT operations telemetry such as events and metrics with machine data analytics Correct answer.

Explanation

Early AIOps platforms were positioned to ingest IT operations telemetry, including events, logs, and metrics, and apply big-data analytics to that machine data. This combination of operational telemetry with analytics is the historical core of the category.

Question 3Origins and history of AIOps

In the historical evolution of IT operations management, what problem did event correlation tools attempt to solve before AIOps emerged?

  1. Automatically generating marketing content for service catalogs
  2. Encrypting all network traffic between data centers
  3. Reducing floods of raw alerts into a smaller set of meaningful incidents Correct answer.
  4. Providing version control for infrastructure configuration files

Explanation

Event correlation and root-cause analysis tools predated AIOps and aimed to collapse alert storms into fewer actionable incidents. AIOps later extended this goal with machine learning and broader data ingestion.

Question 4Origins and history of AIOps

Which sequence best represents the historical progression of IT operations tooling that led toward AIOps?

  1. Spreadsheet reporting, then AIOps platforms, then domain-specific monitoring
  2. Domain-specific monitoring, then centralized event management and correlation, then analytics-driven AIOps platforms Correct answer.
  3. Centralized event management, then AIOps platforms, then domain-specific monitoring
  4. AIOps platforms, then centralized event management, then domain-specific monitoring

Explanation

Operations tooling generally moved from siloed, domain-specific monitoring to centralized event management and correlation, and then to analytics-driven platforms branded as AIOps. Each stage built on the data aggregation of the prior one.

Question 5Origins and history of AIOps

What does the 'Ops' in AIOps refer to historically?

  1. The operations research branch of mathematics
  2. A specific open-source project named Ops
  3. IT operations disciplines responsible for running production services Correct answer.
  4. The operating system kernel development community

Explanation

AIOps applies AI techniques to IT operations, the discipline concerned with running and maintaining production services. The term was framed around the operational practices of monitoring, incident handling, and service management.

Question 6Origins and history of AIOps

Early AIOps discussions distinguished 'algorithmic IT operations' from traditional monitoring mainly by emphasizing which capability?

  1. Continuous learning from operational data to improve detection and diagnosis Correct answer.
  2. Periodic quarterly audits of server inventory
  3. Manual review of every alert by a human operator
  4. Static threshold rules that never change after deployment

Explanation

Algorithmic IT operations emphasized learning from data over time, allowing detection and diagnosis to improve rather than relying solely on fixed rules. Traditional monitoring depended heavily on static thresholds and manual triage.

Question 7Origins and history of AIOps

A team historically relied on per-device threshold alerts and later adopted a platform that clusters related alerts and suggests probable cause. Which shift does this illustrate in the AIOps narrative?

  1. From incident response to software development lifecycle ownership
  2. From cloud computing to mainframe computing
  3. From observability to manual log inspection
  4. From isolated domain monitoring to cross-domain correlation and diagnosis Correct answer.

Explanation

The described shift moves from siloed, threshold-based monitoring toward cross-domain correlation and suggested diagnosis, a central theme in the emergence of AIOps. The platform aggregates signals that were previously handled device by device.

Question 8Origins and history of AIOps

Why was the growth of machine data volume a key historical driver for AIOps?

  1. It guaranteed that all incidents would resolve automatically
  2. It eliminated the need for any monitoring instrumentation
  3. It removed the need for skilled operations engineers
  4. It made manual correlation of all events economically and cognitively impractical Correct answer.

Explanation

As environments generated far more events, logs, and metrics, humans could no longer correlate them manually at scale, creating demand for analytics-driven approaches. This volume problem is a recurring justification for AIOps adoption.

Question 9Origins and history of AIOps

Which historical IT management concept is most directly a precursor to AIOps root-cause analysis?

  1. Desktop patch deployment
  2. Event correlation and root-cause analysis in enterprise systems management Correct answer.
  3. Capacity planning spreadsheets
  4. Asset depreciation accounting

Explanation

Enterprise systems management already included event correlation and root-cause analysis, which AIOps later enhanced with machine learning and broader data sources. The lineage runs from rule-based correlation to probabilistic and learned diagnosis.

Question 10Origins and history of AIOps

In the historical framing, what role did application performance monitoring play in the path toward AIOps?

  1. It provided deep application-level telemetry that later fed analytics platforms Correct answer.
  2. It focused exclusively on physical cabling standards
  3. It replaced all infrastructure monitoring permanently
  4. It was unrelated to operations data collection

Explanation

Application performance monitoring contributed detailed application-level traces and metrics that became valuable input to analytics platforms. AIOps extended this by correlating application telemetry with infrastructure and event data.

Question 11Origins and history of AIOps

Which statement best captures how AIOps relates to earlier IT service management processes?

  1. It augments service management by improving detection, correlation, and diagnosis of incidents Correct answer.
  2. It removes the need for incident records and change management
  3. It applies only to development environments and never to production
  4. It replaces service management processes entirely with unsupervised automation

Explanation

AIOps historically augmented IT service management rather than replacing it, improving how incidents are detected, correlated, and diagnosed. Service management processes such as incident and change management continued to provide the operational framework.

Question 12Origins and history of AIOps

Early vendor positioning of AIOps often combined which two technology trends?

  1. 3D printing and robotics
  2. Big data analytics and machine learning applied to operations Correct answer.
  3. Blockchain and smart contracts
  4. Quantum computing and cryptography

Explanation

The AIOps category emerged at the intersection of big data analytics and machine learning applied to IT operations data. These two trends supplied the capacity to ingest and learn from large operational datasets.

Question 13Origins and history of AIOps

A historian of IT operations notes that pre-AIOps tools produced many false positives. How did analytics-based approaches address this legacy issue?

  1. By disabling monitoring during peak traffic
  2. By suppressing all alerts regardless of severity
  3. By requiring engineers to lower every threshold manually
  4. By learning patterns that distinguish significant anomalies from noise Correct answer.

Explanation

Analytics-based approaches learn normal behavior and patterns so that significant anomalies can be separated from noise, addressing the false-positive problem of static rules. This learning capability is a defining improvement over threshold-only monitoring.

Question 14Origins and history of AIOps

Which factor most plausibly explains why AIOps gained momentum as organizations migrated to dynamic cloud environments?

  1. Cloud migration eliminated the need for cross-team incident coordination
  2. Static thresholds became more reliable as infrastructure grew more elastic
  3. Cloud environments reduced the number of metrics requiring analysis
  4. Elastic, rapidly changing topologies made fixed thresholds and manual correlation less effective Correct answer.

Explanation

Elastic cloud topologies change quickly, so fixed thresholds and manual correlation struggle to keep pace, which increased interest in adaptive analytics. This dynamic context is a commonly cited reason for AIOps adoption.

Question 15Origins and history of AIOps

What distinguishes the historical 'AIOps platform' category from a single-purpose log search tool?

  1. It operates only on offline backups
  2. It ingests and correlates multiple telemetry types to support operational decisions Correct answer.
  3. It only stores logs without any query capability
  4. It is restricted to a single application vendor's stack

Explanation

AIOps platforms are characterized by ingesting and correlating multiple telemetry types, such as events, metrics, and logs, to support operational decisions. A single-purpose log search tool lacks this cross-signal correlation scope.

Question 16Origins and history of AIOps

Which description best matches the early AIOps promise of 'prescriptive' versus merely 'descriptive' analytics?

  1. Descriptive analytics predicts future outages with certainty
  2. Prescriptive analytics moves beyond reporting what happened toward recommending what to do Correct answer.
  3. Prescriptive analytics only visualizes historical dashboards
  4. Descriptive analytics recommends remediation actions automatically

Explanation

Descriptive analytics explains what happened, while prescriptive analytics recommends actions based on analysis. The AIOps narrative often framed increasing maturity as moving from description toward prediction and prescription.

Question 17Origins and history of AIOps

In the historical maturity model often cited for AIOps, which capability typically follows basic monitoring and aggregation?

  1. Manual ticket creation for every raw event
  2. Correlation and anomaly detection across aggregated data Correct answer.
  3. Decommissioning of all telemetry sources
  4. Full autonomous remediation without human oversight

Explanation

After monitoring and aggregation establish a data foundation, the next maturity step is typically correlation and anomaly detection across that aggregated data. Autonomous remediation is a far later and more advanced aspiration.

Question 18Origins and history of AIOps

Why is the term 'AI' in AIOps historically considered broader than generative or large language models?

  1. It was trademarked by a single hardware vendor
  2. It encompasses statistical learning, pattern recognition, and rule-based inference used long before modern language models Correct answer.
  3. It excludes any use of machine learning
  4. It refers only to physical robots used in data centers

Explanation

The AI in AIOps historically covered a broad set of techniques, including statistical learning, pattern recognition, and rule-based inference, well before modern language models existed. Generative models are a recent addition to that toolkit rather than the definition of the term.

Question 19Origins and history of AIOps

A retrospective on AIOps adoption notes that early projects often stalled after alert aggregation. Which explanation is most consistent with the historical record?

  1. Machine learning was legally prohibited in operations
  2. Aggregation alone did not deliver diagnosis or action, so value depended on later analytics maturity Correct answer.
  3. Aggregation made all operational data redundant
  4. Organizations refused to collect any telemetry

Explanation

Aggregating alerts reduces noise but does not by itself diagnose causes or recommend action, so value realization depended on advancing to correlation, detection, and prescription. This gap between aggregation and actionable insight explains many stalled initiatives.

Question 20Origins and history of AIOps

Which historical distinction best separates 'observability' from the original AIOps framing?

  1. Observability forbids the use of metrics and logs
  2. Observability predates all monitoring tools by decades
  3. Observability is a finance practice, while AIOps is a security practice
  4. Observability focuses on inferring internal state from outputs, while AIOps focuses on applying analytics to operational data for decisions Correct answer.

Explanation

Observability concerns inferring internal system state from external outputs, whereas AIOps emphasizes analytics applied to operational data to support decisions. The two concepts overlap but have different historical emphases.

Question 21Origins and history of AIOps

Which statement about the origin of the AIOps label is most accurate?

  1. It was created by an open-source foundation as a coding standard
  2. It was first used to describe database normalization
  3. It originated as a hardware certification program
  4. It was popularized by an industry analyst firm as a market category Correct answer.

Explanation

The AIOps label was popularized by an industry analyst firm as a market category describing analytics-driven IT operations platforms. It was not an open-source standard, hardware certification, or database concept.

Question 22Origins and history of AIOps

Which legacy practice is most associated with the 'runbook automation' tradition that fed into AIOps remediation ambitions?

  1. Conducting employee performance reviews
  2. Encoding repeatable operational procedures for automated execution Correct answer.
  3. Writing marketing copy for product launches
  4. Designing user interface color schemes

Explanation

Runbook automation encoded repeatable operational procedures so they could be executed consistently, forming a foundation for later AIOps remediation ambitions. AIOps sought to trigger and refine such procedures based on analytics.

Question 23Origins and history of AIOps

In the history of operations analytics, what did 'baselining' contribute that later informed AIOps anomaly detection?

  1. Establishing expected behavior ranges against which deviations can be judged Correct answer.
  2. Eliminating the need to collect any historical data
  3. Replacing all human operators with scripts
  4. Guaranteeing that anomalies never occur

Explanation

Baselining established expected behavior ranges so that deviations could be identified, a concept that carried into AIOps anomaly detection. Learning dynamic baselines improved on static historical baselines.

Question 24Origins and history of AIOps

Which scenario best illustrates the historical transition from siloed monitoring to AIOps-style correlation?

  1. Each team watches only its own dashboard and never shares data
  2. Alerts are disabled to reduce dashboard clutter
  3. Network, server, and application signals are combined to identify a shared root cause Correct answer.
  4. A single engineer manually checks each server every morning

Explanation

Combining network, server, and application signals to find a shared root cause exemplifies cross-domain correlation, the hallmark of the AIOps transition. Siloed dashboards and manual checks represent the earlier fragmented approach.

Question 25Origins and history of AIOps

Why did the AIOps narrative emphasize 'noise reduction' as an early, tangible benefit rather than full autonomy?

  1. Noise reduction required no data at all
  2. Noise reduction eliminated the need for monitoring tools
  3. Noise reduction delivered measurable value with existing data, while autonomy required mature analytics and trust Correct answer.
  4. Autonomy was already achieved by all early adopters

Explanation

Noise reduction produced measurable value using data organizations already had, making it an attainable early benefit. Full autonomy demanded more mature analytics, integration, and organizational trust, so it remained a longer-term goal.

Question 26Origins and history of AIOps

Which term did Gartner popularize in 2016 to describe platforms that combine big data and machine learning to enhance IT operations?

  1. AIOps Correct answer.
  2. ITIL v3
  3. DevSecOps
  4. SRE

Explanation

Gartner introduced the AIOps label in 2016 to describe combining big data and machine learning to improve IT operations. The term consolidated earlier algorithmic IT operations ideas under one industry category.

Question 27Origins and history of AIOps

The AIOps label emerged from which broader industry trend in the mid-2010s?

  1. The shift of IT monitoring data into big-data platforms feeding machine learning models Correct answer.
  2. The replacement of all human operators by autonomous robots
  3. The move away from cloud computing toward mainframe-only architectures
  4. The consolidation of all vendors into a single monitoring standard

Explanation

AIOps grew out of the practice of ingesting large volumes of operational telemetry into big-data stores and applying machine learning to it. This reflected real platform convergence rather than vendor standardization or automation of all human roles.

Question 28Origins and history of AIOps

Which two data sources were most commonly combined in the earliest AIOps platforms?

  1. Design mockups with user surveys
  2. Event and alert streams with infrastructure metrics Correct answer.
  3. Source code repositories with payroll records
  4. Marketing analytics with CRM records

Explanation

Early AIOps platforms focused on operational telemetry: events, alerts, and infrastructure or application metrics. These streams were correlated to reduce noise and surface probable causes.

Question 29Origins and history of AIOps

Before the AIOps label existed, academic and vendor work on applying algorithms to IT operations was often described by which phrase?

  1. Chaos engineering
  2. Infrastructure as code
  3. Algorithmic IT operations Correct answer.
  4. Continuous delivery

Explanation

Algorithmic IT operations was an early descriptor for using algorithms to analyze operational data. It predates the AIOps branding and captures the same underlying ambition.

Question 30Origins and history of AIOps

Which capability formed the historical foundation on which later AIOps correlation was built?

  1. Static network diagram maintenance
  2. Manual ticket triage performed by a single engineer
  3. Quarterly capacity planning spreadsheets
  4. Event management that consolidated alerts from multiple monitoring tools Correct answer.

Explanation

Event management platforms aggregated and deduplicated alerts from many tools, creating the consolidated stream that AIOps analytics later consumed. This made it a direct technical precursor.

Question 31Origins and history of AIOps

In the historical narrative, what did 'single pane of glass' consolidation contribute to the path toward AIOps?

  1. It replaced service management processes entirely
  2. It guaranteed root-cause identification without analysis
  3. It eliminated the need for any monitoring tools
  4. It unified fragmented tool views, creating a shared data layer that analytics could exploit Correct answer.

Explanation

Single pane of glass initiatives integrated disparate monitoring consoles into one view. The integration work also produced the unified data foundation that later machine learning could analyze.

Question 32Origins and history of AIOps

Which technology shift in the 2010s most directly enabled machine learning on operational telemetry at scale?

  1. The standardization of manual runbooks
  2. A decline in the number of monitored devices
  3. The retirement of log collection systems
  4. Affordable distributed compute and storage for large datasets Correct answer.

Explanation

Distributed big-data compute and storage made it feasible to retain and process high-volume telemetry. That affordability underpinned practical machine learning in operations.

Question 33Origins and history of AIOps

Historically, which discipline supplied many of the anomaly detection techniques later adopted in AIOps platforms?

  1. Supply chain logistics
  2. Statistical process control and time-series analysis Correct answer.
  3. Graphic design
  4. Contract law

Explanation

Anomaly detection in AIOps draws on statistical process control and time-series analysis traditions. These methods established baselines and deviation thresholds long before AIOps branding.

Question 34Origins and history of AIOps

A company in 2015 replaces per-device threshold alerts with a platform that learns normal behavior per service and groups related incidents. Which historical transition does this best illustrate?

  1. From observability to mainframe batch processing
  2. From service management to manual ticketing
  3. From static threshold monitoring to behavioral, correlation-based operations analytics Correct answer.
  4. From cloud computing to on-premises hosting

Explanation

The scenario shows a move away from fixed thresholds toward learned baselines and incident grouping, the core shift AIOps represents. It is an application of behavioral analytics to operations.

Question 35Origins and history of AIOps

Why did early AIOps vendors emphasize ingesting data from third-party monitoring tools rather than replacing them?

  1. Replacement was technically impossible in every case
  2. Customers had large existing tool investments, so ingestion offered value without rip-and-replace Correct answer.
  3. Third-party tools were legally required to remain in place
  4. Machine learning could not process native telemetry

Explanation

Enterprises had substantial sunk investment in monitoring suites, so AIOps platforms positioned themselves as an analytics layer above existing tools. This lowered adoption friction.

Question 36Origins and history of AIOps

Which historical criticism was most often raised about early AIOps correlation engines?

  1. They required no data to function
  2. Models produced plausible but opaque groupings that operators struggled to trust Correct answer.
  3. They worked only on mainframe systems
  4. They eliminated all alert volume instantly

Explanation

Early correlation engines often lacked transparency, making their groupings hard for operators to validate. Trust and explainability became recurring adoption obstacles.

Question 37Origins and history of AIOps

How did the rise of microservices influence the historical evolution toward AIOps?

  1. It reduced the number of components needing monitoring
  2. It standardized all telemetry formats across vendors
  3. It made logs unnecessary for diagnosis
  4. It multiplied dynamic service dependencies, making manual correlation infeasible Correct answer.

Explanation

Microservices increased the number and dynamism of service interactions, overwhelming manual correlation. This complexity pushed organizations toward automated analytics.

Question 38Origins and history of AIOps

Which statement best describes the historical relationship between AIOps and the concept of closed-loop automation?

  1. Closed-loop automation predated and replaced AIOps entirely
  2. AIOps limited itself to dashboard visualization
  3. AIOps analytics were envisioned as feeding automated remediation, forming a detect-analyze-act loop Correct answer.
  4. AIOps explicitly rejected any form of automation

Explanation

AIOps was framed as the analytics brain that could trigger automated remediation, closing the loop from detection to action. Early implementations often stopped short of full automation but retained this ambition.

Question 39Origins and history of AIOps

In the history of AIOps, what did 'topology-aware correlation' add beyond simple alert grouping?

  1. It used service dependency maps to relate symptoms to likely upstream causes Correct answer.
  2. It converted all metrics into text logs
  3. It removed the need for any alert data
  4. It disabled incident prioritization

Explanation

Topology awareness let correlation engines trace symptoms along dependency paths toward probable sources. This improved causal reasoning compared with grouping alerts by timing alone.

Question 40Origins and history of AIOps

Which early use case most commonly delivered the first measurable AIOps return on investment?

  1. Designing user interfaces for monitoring consoles
  2. Reducing duplicate and low-value alert volume reaching on-call engineers Correct answer.
  3. Replacing all database administrators
  4. Automating financial reporting

Explanation

Alert noise reduction was tangible and quick to measure, making it the typical first ROI story. It required correlation and deduplication rather than full autonomous remediation.

Question 41Origins and history of AIOps

A retailer's operations team in 2018 combines logs, metrics, and traces in one analytics platform to explain a checkout slowdown. Which historical concept does this most resemble?

  1. Waterfall change management
  2. Converged observability building on AIOps data consolidation Correct answer.
  3. Mainframe capacity planning
  4. Manual configuration management

Explanation

Combining logs, metrics, and traces for explanation reflects converged observability, which inherited AIOps data consolidation practices. It extends the analytics tradition with richer telemetry.

Question 42Origins and history of AIOps

Why did the AIOps narrative historically emphasize augmented decision support over fully autonomous operations?

  1. Regulators banned all automated remediation worldwide
  2. Autonomy was technically solved but commercially unwanted
  3. Machine learning could not produce any recommendations
  4. Operators retained accountability, so recommendations needed human validation before action Correct answer.

Explanation

Accountability for production systems stayed with human operators, so early AIOps framed itself as decision support. Recommendations were surfaced for validation rather than executed blindly.

Question 43Origins and history of AIOps

Which historical precedent most directly shaped AIOps incident enrichment, where context is attached to alerts automatically?

  1. Manual whiteboard incident reviews
  2. Annual disaster recovery tests
  3. Runbook-linked diagnostic scripts that gathered context during troubleshooting Correct answer.
  4. Desktop patch management

Explanation

Diagnostic scripts attached to runbooks gathered contextual data during incidents, a precursor to automated enrichment. AIOps generalized this by pulling context from many sources programmatically.

Question 44Origins and history of AIOps

What role did open-source telemetry projects play in the historical spread of AIOps practices?

  1. They eliminated the need for data storage
  2. They replaced machine learning with manual inspection
  3. They prohibited commercial AIOps platforms
  4. They lowered the cost of collecting and standardizing metrics, logs, and traces for analytics Correct answer.

Explanation

Open-source collection and standardization projects reduced entry costs and created common data formats. This made analytics adoption easier across diverse environments.

Question 45Origins and history of AIOps

A bank's 2014 pilot used statistical baselining to detect unusual transaction latency, years before adopting an AIOps-branded suite. How should this be characterized historically?

  1. A purely manual monitoring approach
  2. An early algorithmic operations practice consistent with later AIOps principles Correct answer.
  3. A contradiction of AIOps principles
  4. A return to mainframe batch processing

Explanation

Statistical baselining for anomaly detection is a core AIOps technique even when the label is absent. The practice predates branding and demonstrates continuity in the field.

Question 46Origins and history of AIOps

Which historical factor best explains why AIOps adoption accelerated in organizations running hybrid cloud estates?

  1. Hybrid estates eliminated all operational data
  2. Telemetry spanned environments with inconsistent tools, so a unifying analytics layer reduced fragmentation Correct answer.
  3. Vendors refused to support cloud environments
  4. On-premises tools automatically covered cloud workloads

Explanation

Hybrid estates produced heterogeneous telemetry that was hard to interpret holistically. A unifying analytics layer addressed fragmentation, driving adoption.

Question 47Origins and history of AIOps

Which statement most accurately reflects the historical evolution of AIOps from descriptive to predictive analytics?

  1. Descriptive and predictive analytics were never distinguished in the field
  2. Platforms began with predictive forecasting and later dropped descriptive reporting
  3. Platforms began by summarizing what happened and gradually added forecasting of likely failures Correct answer.
  4. Predictive analytics was abandoned as impractical in every deployment

Explanation

AIOps maturity moved from summarizing past events toward forecasting future conditions. Descriptive foundations were necessary before predictive models could be trusted.

Question 48Origins and history of AIOps

Historically, what distinguished AIOps correlation from simple alert deduplication?

  1. Correlation inferred relationships among distinct events, while deduplication only removed identical repeats Correct answer.
  2. Deduplication used machine learning while correlation did not
  3. Correlation required no event data at all
  4. Deduplication produced root-cause hypotheses

Explanation

Deduplication removes repeated identical alerts, whereas correlation reasons about relationships among different events. The latter is a deeper analytical step that AIOps emphasized.

Question 49Origins and history of AIOps

Which historical explanation best accounts for why AIOps platforms initially struggled with rapidly changing cloud-native environments?

  1. Cloud-native environments produced no telemetry
  2. Models trained on stable baselines degraded when infrastructure churned frequently Correct answer.
  3. Cloud providers blocked telemetry collection
  4. Operators refused to use any analytics

Explanation

Frequently changing infrastructure invalidated learned baselines, causing model drift. Early platforms designed for stable estates needed adaptation to dynamic environments.

Question 50Origins and history of AIOps

In the historical framing, how did AIOps platforms differ from earlier business intelligence systems applied to IT data?

  1. AIOps avoided all data storage
  2. AIOps targeted real-time operational decisions, while BI emphasized periodic reporting Correct answer.
  3. BI systems operated only on network packets
  4. AIOps rejected statistical methods entirely

Explanation

AIOps was oriented toward real-time operational response, whereas BI focused on periodic analytical reporting. The latency and action orientation distinguished the two.