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[Hardware] Exam Dumps C1000-189 Provider | C1000-189 Exam Brain Dumps

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【Hardware】 Exam Dumps C1000-189 Provider | C1000-189 Exam Brain Dumps

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IBM Instana Observability v1.0.277 Administrator - Professional Sample Questions (Q40-Q45):NEW QUESTION # 40
What is Instana's custom built software that is designed to monitor a specified technology?
  • A. Service
  • B. Sensor
  • C. Profiling
  • D. Tracer
Answer: B
Explanation:
Instana uses Sensors as specialized software components embedded within its agents to monitor and extract telemetry from various supported technologies. The verified documentation states: "Sensors are built-in modules that detect, identify, and monitor specific technologies such as databases, servers, run-times, and messaging systems." These components ensure that the agent collects targeted metrics, events, and traces optimized for individual stacks like MySQL, Kafka, or Java. When deployed, the Instana agent automatically discovers technologies running in the environment and loads corresponding Sensors dynamically, requiring minimal user configuration. Tracers handle transaction propagation, Profiling covers code-level performance, and Service is a higher abstraction in application topology-not individual monitoring logic. The Sensor concept remains core to Instana's automatic discovery and observability architecture as validated in IBM's architectural reference sections.

NEW QUESTION # 41
In context of Golden Signals in Instana monitoring, what is the true definition of latency?
  • A. How long does it take to open a webpage
  • B. How many errors are there in one HTTP request
  • C. How long does it take to login to mobile application
  • D. How long it takes to handle or service a particular request
Answer: D
Explanation:
Latency is one of the four principal Golden Signals monitored in Instana and critical for measuring system performance and user experience. According to IBM Instana Observability documentation: "Latency is the time it takes to handle or service a request, measured as the duration between request start and response end." This applies regardless of protocol (HTTP, RPC, messaging, etc.) and is used to evaluate whether services are fast or slow under real load. Instana automatically tracks latency for every transaction, as shown in traces and metrics: this enables teams to identify slow services, resource contention, and downstream delays. Golden Signals (latency, error rate, traffic, saturation) provide a universal framework recognized in both SRE and performance engineering disciplines. The actual duration a user spends logging in or opening a webpage may be an instance of latency, but Instana's definition is generalized to any service request (API, DB, etc.), not just interactive browser events. Error count is monitored separately (error signal).

NEW QUESTION # 42
When installing the Instana host agent on Kubernetes, which option is valid?
  • A. Binary
  • B. RPM
  • C. Homebrew
  • D. Operator
Answer: D
Explanation:
The Instana Operator is the officially recommended and supported method for deploying the Instana host agent on Kubernetes clusters. The IBM Instana Observability documentation states, "The recommended method to install the Instana agent on Kubernetes clusters is via the Instana Operator, which uses Custom Resources to simplify lifecycle management." The Operator pattern in Kubernetes automates not just installation, but also upgrades, configuration, and management of agents across the entire cluster. This ensures security and reliability because the Operator reacts to cluster changes and can self-heal agent deployments. Other install options such as Homebrew, direct binary, or RPM are for traditional VM or bare-metal hosts-not for orchestrated container environments like Kubernetes. Only with the Operator does Instana support automated scaling, configuration through CRDs, and native Kubernetes best practices. Helm charts are also often involved in configuring the Operator, further streamlining agents' deployment in public, private, or hybrid cloud clusters.

NEW QUESTION # 43
Which two filters can be used in scheduling maintenance windows to mute affected entities?
  • A. Dynamic Focus
  • B. Scope based
  • C. Application Perspective
  • D. Custom Entity
  • E. Smart Alerts
Answer: B,C
Explanation:
Scheduling maintenance windows in IBM Instana Observability allows teams to define planned downtimes or service windows without triggering false alerts. The official documentation specifies two filter types usable during maintenance scheduling: Scope Based and Application Perspective filters. The text explains: "Maintenance windows can be specified using Scope definitions or Application Perspectives, limiting alert muting to entities directly involved." Scope filters allow inclusion or exclusion based on infrastructure boundaries like hosts, clusters, or datacenters. Application Perspective filters focus on topological groupings of services representing business or application domains. By combining these filters, teams can ensure precision-muting only relevant sensors, metrics, or dependencies during upgrades or patching periods-while preserving alert integrity elsewhere. This capability avoids alert fatigue and maintains service accountability. Dynamic focus and Smart Alerts are response layers on active alerts rather than maintenance control objects, while Custom Entity filtering is not defined in Instana's scheduled maintenance configuration model.

NEW QUESTION # 44
Which two thresholds can be chosen in Advanced Mode when setting up a Smart Alert?
  • A. Progressive
  • B. Static
  • C. Adaptive
  • D. Single
  • E. Neutral
Answer: B,C
Explanation:
Instana Smart Alerts provide intelligent, context-aware alerting capabilities. In Advanced Mode, administrators can choose between two distinct threshold types: Static and Adaptive. The IBM Instana documentation details: "Advanced Mode supports both static and adaptive thresholds, letting users define explicit limit values or rely on adaptive baselines derived from historical data." Static thresholds are fixed, user-defined values best suited for predictable workloads or regulatory uptime scenarios. Adaptive thresholds use machine learning on time-series historical behavior to automatically adjust boundaries when traffic patterns or operating baselines change. This significantly reduces false positives and ensures that alerts reflect true anomalies rather than normal variance. Both threshold types can trigger multi-level alerts and integrate with escalation policies. Static measures remain critical for SLIs requiring consistent control, while adaptive techniques optimize monitoring of microservices under fluctuating loads. IBM emphasizes combining these in practice to balance detected sensitivity across mixed systems, leveraging AI-driven dynamic configurations in adaptive mode as a key differentiator in its observability platform.

NEW QUESTION # 45
......
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