Kibana provides a web interface to visualize, search, and analyze data stored in Elasticsearch.
Incorrect! Try again.
9Which of the following are considered the three pillars of observability?
Observability: Metrics, Logs, Traces
Easy
A.Metrics, Logs, and Traces
B.Users, Servers, and Networks
C.Code, Tests, and Builds
D.Files, Folders, and Drives
Correct Answer: Metrics, Logs, and Traces
Explanation:
The three pillars of observability are metrics, logs, and traces, which together provide insight into system behavior.
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10What do 'traces' primarily help track in a system?
Observability: Metrics, Logs, Traces
Easy
A.The color of dashboards
B.The path of a request across services
C.The number of employees
D.The size of source files
Correct Answer: The path of a request across services
Explanation:
Traces follow a request as it moves through different services, helping identify latency and bottlenecks.
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11Which observability pillar consists of numeric measurements collected over time?
Observability: Metrics, Logs, Traces
Easy
A.Tickets
B.Logs
C.Traces
D.Metrics
Correct Answer: Metrics
Explanation:
Metrics are numeric measurements, such as CPU usage or request counts, collected over time.
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12What is the main goal of DevSecOps?
Introduction to DevSecOps
Easy
A.Integrating security into the DevOps process
B.Removing testing from pipelines
C.Slowing down deployments
D.Replacing developers with tools
Correct Answer: Integrating security into the DevOps process
Explanation:
DevSecOps integrates security practices into every stage of the DevOps lifecycle rather than adding them at the end.
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13In DevSecOps, security is best described as being handled at which stage?
Introduction to DevSecOps
Easy
A.Only during design
B.Never during development
C.Only after deployment
D.Throughout the entire development lifecycle
Correct Answer: Throughout the entire development lifecycle
Explanation:
DevSecOps applies the principle of 'shift-left', embedding security across all stages of development.
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14What does the acronym SRE stand for?
Introduction to Site Reliability Engineering (SRE)
Easy
A.System Resource Estimation
B.Software Release Editor
C.Site Reliability Engineering
D.Secure Runtime Environment
Correct Answer: Site Reliability Engineering
Explanation:
SRE stands for Site Reliability Engineering, a discipline focused on system reliability using software engineering practices.
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15Which company originally introduced the concept of Site Reliability Engineering?
Introduction to Site Reliability Engineering (SRE)
Easy
A.Microsoft
B.Netflix
C.Google
D.Amazon
Correct Answer: Google
Explanation:
Site Reliability Engineering was pioneered by Google to apply software engineering to operations and reliability.
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16Which of the following is a widely recommended DevOps best practice?
DevOps Best Practices
Easy
A.Automating repetitive tasks
B.Avoiding all version control
C.Deploying only once a year
D.Skipping monitoring
Correct Answer: Automating repetitive tasks
Explanation:
Automation of repetitive tasks reduces errors, saves time, and is a core DevOps best practice.
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17Which practice encourages frequent, small code changes in DevOps?
DevOps Best Practices
Easy
A.Manual deployment
B.Continuous Integration
C.Annual releases
D.Isolated development
Correct Answer: Continuous Integration
Explanation:
Continuous Integration promotes frequent, small code merges with automated testing to catch issues early.
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18What is Netflix's Chaos Engineering primarily used for?
Real Industry Case Studies: Google SRE, Netflix Chaos Engineering
Easy
A.Writing new movies
B.Managing subscriptions
C.Designing user profiles
D.Testing system resilience by injecting failures
Correct Answer: Testing system resilience by injecting failures
Explanation:
Chaos Engineering deliberately introduces failures to test how well systems recover and remain resilient.
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19Which famous tool did Netflix create for Chaos Engineering?
Real Industry Case Studies: Google SRE, Netflix Chaos Engineering
Easy
A.Chaos Monkey
B.Kibana
C.Logstash
D.Prometheus
Correct Answer: Chaos Monkey
Explanation:
Netflix created Chaos Monkey, a tool that randomly terminates instances to test system resilience.
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20What is the core idea behind GitOps?
Future of DevOps: GitOps, AIOps, NoOps
Easy
A.Using Git as the single source of truth for infrastructure and deployments
B.Using Git only for chatting
C.Storing only images in Git
D.Removing Git from all workflows
Correct Answer: Using Git as the single source of truth for infrastructure and deployments
Explanation:
GitOps uses Git repositories as the single source of truth to manage infrastructure and application deployments.
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21A team notices that their application meets all uptime targets but users still complain about slow checkout times during peak hours. Which monitoring gap does this scenario most directly highlight?
Need for Monitoring in DevOps
Medium
A.Monitoring infrastructure but not the CI/CD pipeline duration
B.Using too many alerting rules for the checkout service
C.Relying only on availability metrics while ignoring performance and latency metrics
D.Collecting logs without retaining them long enough
Correct Answer: Relying only on availability metrics while ignoring performance and latency metrics
Explanation:
Uptime alone does not capture user experience. Monitoring must include latency and performance metrics to detect degradation that uptime checks miss.
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22In a Prometheus-based setup, an engineer wants to scrape metrics from a short-lived batch job that exits before Prometheus can poll it. Which component should be used?
Monitoring Tools: Prometheus & Grafana (overview)
Medium
A.Grafana Loki
B.Node Exporter
C.Pushgateway
D.Alertmanager
Correct Answer: Pushgateway
Explanation:
Prometheus uses a pull model, so ephemeral jobs push their metrics to the Pushgateway, which Prometheus then scrapes.
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23A team uses Prometheus for data collection and Grafana for visualization. Which statement correctly describes the division of responsibility?
Monitoring Tools: Prometheus & Grafana (overview)
Medium
A.Both tools store data independently and cannot share a data source
B.Prometheus stores and queries time-series data while Grafana renders dashboards from that data
C.Prometheus renders dashboards and Grafana handles alert routing exclusively
D.Grafana stores the time-series data while Prometheus only draws dashboards
Correct Answer: Prometheus stores and queries time-series data while Grafana renders dashboards from that data
Explanation:
Prometheus is the metrics database and query engine; Grafana connects to it as a data source and visualizes the results.
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24Which PromQL expression correctly calculates the per-second rate of HTTP requests over the last 5 minutes for a counter named http_requests_total?
Monitoring Tools: Prometheus & Grafana (overview)
Medium
A.avg_over_time(http_requests_total[5m])
B.rate(http_requests_total[5m])
C.sum(http_requests_total)
D.increase(http_requests_total)
Correct Answer: rate(http_requests_total[5m])
Explanation:
rate() computes the per-second average rate of increase of a counter over the specified time window, making it ideal for request-rate calculations.
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25In the ELK stack, an engineer needs to parse raw unstructured log lines, extract fields, and enrich them before storage. Which component is primarily responsible for this?
Logging Fundamentals: ELK Stack (Elasticsearch, Logstash, Kibana)
Medium
A.Kibana
B.Logstash
C.Elasticsearch
D.Beats
Correct Answer: Logstash
Explanation:
Logstash ingests, parses, transforms, and enriches log data (often using filters like grok) before sending it to Elasticsearch for indexing.
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26A team wants a lightweight agent installed on hundreds of servers to forward logs to Logstash without heavy processing. Which tool best fits this requirement?
Logging Fundamentals: ELK Stack (Elasticsearch, Logstash, Kibana)
Medium
A.Kibana
B.Elasticsearch
C.Grafana
D.Filebeat
Correct Answer: Filebeat
Explanation:
Filebeat is a lightweight shipper from the Beats family designed to forward logs efficiently with minimal resource usage.
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27Which role does Elasticsearch play within the ELK stack?
Logging Fundamentals: ELK Stack (Elasticsearch, Logstash, Kibana)
Medium
A.Visualization layer for building interactive dashboards
B.Agent that collects logs from edge servers
C.Distributed search and analytics engine that indexes and stores log data
D.Pipeline that parses and transforms incoming data
Correct Answer: Distributed search and analytics engine that indexes and stores log data
Explanation:
Elasticsearch stores and indexes the data, enabling fast full-text search and analytics that Kibana then visualizes.
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28A microservice request passes through five services and fails intermittently. Which observability pillar is most useful for pinpointing exactly which service in the request path introduced the latency?
Observability: Metrics, Logs, Traces
Medium
A.Raw log counts
B.Aggregated metrics
C.Distributed traces
D.Static dashboards
Correct Answer: Distributed traces
Explanation:
Traces follow a single request across services, showing timing at each hop, which is ideal for locating where latency or failure occurs.
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29Which statement best distinguishes metrics from logs in an observability strategy?
Observability: Metrics, Logs, Traces
Medium
A.Metrics and logs are identical and interchangeable in practice
B.Metrics store full request payloads, while logs only store numeric counters
C.Logs are always cheaper to store than metrics at any scale
D.Metrics are numeric aggregates efficient for trends, while logs are discrete event records rich in detail
Correct Answer: Metrics are numeric aggregates efficient for trends, while logs are discrete event records rich in detail
Explanation:
Metrics are compact numeric time-series good for trends and alerts; logs are detailed event records useful for root-cause investigation.
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30A team says their system is "monitored" but they cannot explain unexpected new failure modes without adding new code. What does this indicate about their setup?
Observability: Metrics, Logs, Traces
Medium
A.They have too much observability and should reduce instrumentation
B.Their traces are consuming all metrics storage
C.They have monitoring but lack true observability into unknown-unknowns
D.They have replaced logging entirely with metrics
Correct Answer: They have monitoring but lack true observability into unknown-unknowns
Explanation:
Monitoring watches known conditions; observability lets you ask new questions about unforeseen issues without shipping new code. This team lacks the latter.
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31Which practice best embodies the "shift-left" principle of DevSecOps?
Introduction to DevSecOps
Medium
A.Running automated security scans within the CI pipeline on every commit
B.Performing a manual security audit only after production release
C.Disabling security tests to speed up deployments
D.Delegating all security to a separate team at the end of the cycle
Correct Answer: Running automated security scans within the CI pipeline on every commit
Explanation:
Shift-left means integrating security early and continuously. Automated scans on each commit catch vulnerabilities before they reach production.
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32A pipeline scans source code for vulnerabilities without executing it, analyzing the code structure directly. Which type of testing is this?
SAST analyzes source code or binaries without running the application, while DAST tests a running application from the outside.
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33A service has an SLO of 99.9% availability over 30 days. Roughly how much error budget (allowed downtime) does this permit in that period?
Introduction to Site Reliability Engineering (SRE)
Medium
A.About 43 minutes
B.About 5 minutes
C.About 7 hours
D.About 24 hours
Correct Answer: About 43 minutes
Explanation:
99.9% of 30 days allows 0.1% downtime. minutes of error budget.
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34In SRE, what is the primary purpose of an error budget?
Introduction to Site Reliability Engineering (SRE)
Medium
A.To measure the number of engineers on call
B.To track the cloud infrastructure cost per month
C.To eliminate all outages permanently
D.To balance reliability with the pace of feature releases
Correct Answer: To balance reliability with the pace of feature releases
Explanation:
An error budget quantifies acceptable unreliability, letting teams ship features until the budget is spent, then prioritize stability.
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35Which pairing correctly orders these SRE reliability terms from customer commitment to internal target to measured value?
Introduction to Site Reliability Engineering (SRE)
Medium
A.SLI → SLO → SLA
B.SLA → SLO → SLI
C.SLA → SLI → SLO
D.SLO → SLA → SLI
Correct Answer: SLA → SLO → SLI
Explanation:
An SLA is the external contract, an SLO is the internal target usually stricter than the SLA, and an SLI is the actual measured indicator.
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36A team commits small changes frequently to a shared branch and runs automated builds and tests on each commit. Which DevOps practice are they primarily following?
DevOps Best Practices
Medium
A.Blue-green deployment
B.Infrastructure as Code
C.Chaos engineering
D.Continuous Integration
Correct Answer: Continuous Integration
Explanation:
Continuous Integration involves merging small changes frequently with automated builds and tests to catch integration issues early.
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37Why is treating infrastructure as version-controlled code (IaC) considered a DevOps best practice?
DevOps Best Practices
Medium
A.It permanently prevents all production incidents
B.It makes environments reproducible, auditable, and consistent across stages
C.It eliminates the need for monitoring tools
D.It removes the need for any testing before deployment
Correct Answer: It makes environments reproducible, auditable, and consistent across stages
Explanation:
IaC lets infrastructure be versioned, reviewed, and recreated reliably, reducing configuration drift and manual errors.
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38Google SRE caps the amount of manual, repetitive operational work engineers do so they can focus on engineering. What is this repetitive work called?
Real Industry Case Studies: Google SRE
Medium
A.Churn
B.Drift
C.Slack
D.Toil
Correct Answer: Toil
Explanation:
Google defines toil as manual, repetitive, automatable operational work. SRE aims to keep toil below a threshold (often ~50%) to preserve engineering time.
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39Netflix's Chaos Monkey randomly terminates production instances. What is the main engineering goal of this practice?
Real Industry Case Studies: Netflix Chaos Engineering
Medium
A.To verify that systems are resilient and can tolerate failures gracefully
B.To slow down deployments so bugs are caught manually
C.To reduce cloud infrastructure billing by shutting down servers
D.To replace automated monitoring with manual checks
Correct Answer: To verify that systems are resilient and can tolerate failures gracefully
Explanation:
Chaos Monkey deliberately injects failures to expose weaknesses, ensuring the system degrades gracefully and recovers automatically.
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40In a GitOps workflow, how is the desired state of infrastructure and applications typically applied to a cluster?
Future of DevOps: GitOps, AIOps, NoOps
Medium
A.A controller continuously reconciles the cluster to match declarations stored in a Git repository
B.Changes are emailed to operators who apply them by hand
C.Engineers manually run kubectl commands directly against production
D.A monitoring tool randomly adjusts the cluster state
Correct Answer: A controller continuously reconciles the cluster to match declarations stored in a Git repository
Explanation:
GitOps uses Git as the single source of truth; an automated agent (e.g., Argo CD or Flux) reconciles the live state to match the repository.
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41In a Prometheus setup scraping 5000 targets every 15s, an SRE observes that rate(http_requests_total[1m]) produces erratic spikes while rate(http_requests_total[5m]) is smooth. What is the most accurate explanation?
Monitoring Tools: Prometheus & Grafana (overview)
Hard
A.Grafana downsamples the 5m query on the client side, which removes all high-frequency variation
B.The 5m window silently discards counter resets, so it always appears smoother than reality
C.The 1m window contains too few scrape samples, making the rate sensitive to individual sample jitter and counter resets
D.Prometheus cannot compute rate() on windows shorter than the global scrape interval regardless of target count
Correct Answer: The 1m window contains too few scrape samples, making the rate sensitive to individual sample jitter and counter resets
Explanation:
With a 15s scrape interval, a 1m window holds only ~4 samples, so rate() extrapolation is highly sensitive to timing jitter. A 5m window (~20 samples) averages over more data, smoothing the result. This is a fundamental sampling trade-off, not a client-side or discard behavior.
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42A team has metrics, logs, and distributed traces but still cannot answer why a specific user's request was slow. Which gap in their observability practice is the most likely root cause?
Observability: Metrics, Logs, Traces
Hard
A.Their metrics have insufficient cardinality to store one series per user
B.They collect logs in JSON rather than plain text, preventing correlation
C.They are using pull-based metrics instead of push-based metrics
D.Traces and logs are not correlated via a shared context such as a propagated trace/span ID
Correct Answer: Traces and logs are not correlated via a shared context such as a propagated trace/span ID
Explanation:
The three pillars only deliver value when linked. Without a propagated trace/span ID injected into logs, you cannot pivot from a slow trace to the exact log lines for that request. High per-user metric cardinality is actually an anti-pattern, and log format alone is not the blocker.
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43During a traffic surge, Logstash falls behind and Elasticsearch shows rejected bulk requests. Which architectural change best addresses back-pressure without dropping logs?
Logging Fundamentals: ELK Stack (Elasticsearch, Logstash, Kibana)
Hard
A.Insert a durable buffer such as Kafka or Redis between shippers and Logstash to decouple ingestion from indexing
B.Increase the Logstash pipeline.workers beyond the number of CPU cores to force higher throughput
C.Move Kibana to a separate node so it stops competing with Logstash for memory
D.Disable Elasticsearch replicas permanently so indexing has less overhead
Correct Answer: Insert a durable buffer such as Kafka or Redis between shippers and Logstash to decouple ingestion from indexing
Explanation:
A message queue absorbs bursts and provides durability, letting Logstash and Elasticsearch consume at a sustainable rate. Over-provisioning workers past CPU count causes contention, permanently disabling replicas risks data loss, and Kibana placement does not affect ingestion throughput.
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44A service has an SLO of 99.9% availability over 30 days. After 12 days it has already consumed 80% of its error budget. According to SRE principles, what is the correct action?
Introduction to Site Reliability Engineering (SRE)
Hard
A.Ignore the budget since it resets at the end of the 30-day window
B.Page the on-call engineer continuously until availability returns to 100%
C.Immediately raise the SLO to 99.99% to give the team more room
D.Freeze risky feature releases and prioritize reliability work until the budget recovers
Correct Answer: Freeze risky feature releases and prioritize reliability work until the budget recovers
Explanation:
Error budgets govern the pace of change. Burning 80% in 40% of the window signals excessive risk, so SRE prescribes slowing releases and investing in reliability. Raising the SLO hides the problem, and chasing 100% availability contradicts the purpose of an error budget.
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45A pipeline runs SAST, DAST, SCA, and secrets scanning. A subtle vulnerability arises from how two independently-safe microservices interact at runtime under load. Which control is most likely to catch it?
Introduction to DevSecOps
Hard
A.DAST and runtime/IAST testing against a deployed, integrated environment
B.SCA, because interaction bugs originate in third-party dependencies
C.Secrets scanning, since inter-service auth relies on shared credentials
D.SAST, because it analyzes all source code paths statically
Correct Answer: DAST and runtime/IAST testing against a deployed, integrated environment
Explanation:
Emergent runtime interaction flaws are invisible to static analysis of individual services. DAST/IAST exercise the running, integrated system and can surface behavior that only appears under real execution and load. SAST and SCA inspect code/dependencies in isolation.
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46Which statement best captures the core scientific discipline that distinguishes chaos engineering from simply 'breaking things in production'?
Netflix Chaos Engineering
Hard
A.You form a hypothesis about steady-state behavior, then inject failure to test whether that steady state holds
B.You randomly terminate as many instances as possible to maximize test coverage
C.You disable monitoring during experiments to observe raw system behavior
D.You only run experiments in staging to guarantee zero customer impact
Correct Answer: You form a hypothesis about steady-state behavior, then inject failure to test whether that steady state holds
Explanation:
Chaos engineering is empirical: define a measurable steady state, hypothesize it will persist through a fault, minimize blast radius, and validate. Random maximal destruction, staging-only scope, or disabling monitoring all violate its principles of controlled, observable experimentation.
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47In a GitOps model using a pull-based reconciliation agent, an operator manually runs kubectl scale to increase replicas during an incident. What happens and why?
Future of DevOps: GitOps, AIOps, NoOps
Hard
A.Nothing happens because pull-based agents only apply changes at deploy time
B.The agent detects drift from the Git-declared state and reverts the replica count back
C.The manual change is permanently accepted because live cluster state overrides Git
D.The agent merges the manual change into Git automatically to preserve it
Correct Answer: The agent detects drift from the Git-declared state and reverts the replica count back
Explanation:
GitOps treats Git as the single source of truth and continuously reconciles live state to match it. A manual kubectl change is drift and will be reverted. To make it stick, the operator must commit the change to Git.
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48An engineer creates a label user_id on a Prometheus metric to track per-user latency across millions of users. Why is this considered a serious anti-pattern?
Monitoring Tools: Prometheus & Grafana (overview)
Hard
A.It causes a cardinality explosion, creating a separate time series per label combination and overwhelming memory
B.Labels can only hold numeric values, so user_id strings are silently dropped
C.Prometheus rejects any metric with more than 100 distinct label values by default
D.Grafana cannot render dashboards for metrics that include user identifiers
Correct Answer: It causes a cardinality explosion, creating a separate time series per label combination and overwhelming memory
Explanation:
Each unique label-value combination is a distinct time series. High-cardinality labels like user_id multiply series into the millions, exhausting Prometheus memory and query performance. Per-user detail belongs in logs or traces, not metric labels.
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49Google's SRE model caps operational 'toil' at roughly 50% of an SRE's time. What is the primary strategic reason for this ceiling?
Google SRE
Hard
A.It is a legal labor requirement limiting operational workload hours
B.It matches the maximum uptime achievable by any distributed system
C.It ensures every SRE spends exactly half their time on manual pager duty
D.It guarantees engineers have time for automation and engineering work that reduces future toil
Correct Answer: It guarantees engineers have time for automation and engineering work that reduces future toil
Explanation:
The 50% cap protects engineering capacity so SREs can automate away repetitive manual work rather than being consumed by it. Unchecked toil scales linearly with growth; investing in engineering breaks that cycle. It is not a legal rule or an uptime metric.
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50A system emits 10 million spans/hour, and storing all of them is cost-prohibitive. The team wants to keep traces of slow or errored requests. Which sampling strategy fits best?
Observability: Metrics, Logs, Traces
Hard
A.Uniform random sampling at a fixed 1% rate applied per span
B.No sampling, relying on Elasticsearch index compression to reduce cost
C.Head-based sampling, which decides at the first span before the outcome is known
D.Tail-based sampling, which decides after a trace completes based on latency or error attributes
Correct Answer: Tail-based sampling, which decides after a trace completes based on latency or error attributes
Explanation:
Tail-based sampling buffers spans and makes retention decisions once the full trace is known, letting you keep all slow/errored traces while dropping normal ones. Head-based and uniform sampling decide before the outcome is visible, so they cannot selectively preserve interesting traces.
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51Consider the relationship between availability nines and downtime. Which pairing of monthly (30-day) allowable downtime is correct?
Introduction to Site Reliability Engineering (SRE)
Hard
A 30-day month has minutes. At 99.9%, allowed downtime is min. Each additional nine reduces this tenfold, giving min at 99.99%.
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52A security team wants to enforce that no build proceeds if a critical CVE is found, but developers complain about excessive false-positive build failures. What is the most balanced DevSecOps approach?
Introduction to DevSecOps
Hard
A.Block all builds on any finding regardless of severity to maximize safety
B.Tune policies to fail only on exploitable, high-confidence findings while reporting lower-severity issues without blocking
C.Remove the security gate entirely and rely on periodic manual audits
D.Move all scanning to production so it never blocks the build pipeline
Correct Answer: Tune policies to fail only on exploitable, high-confidence findings while reporting lower-severity issues without blocking
Explanation:
Effective DevSecOps calibrates gates by severity and confidence so pipelines block on genuine critical risks while non-blocking findings still get visibility. Blocking on everything erodes trust and encourages bypasses; removing gates or deferring to production abandons shift-left security.
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53An organization has excellent uptime dashboards but repeatedly suffers outages that dashboards did not predict. Which monitoring gap most directly explains this?
Need for Monitoring in DevOps
Hard
A.They store metrics for too long, diluting recent data with historical noise
B.They monitor only known failure symptoms and lack leading indicators like saturation and error-rate trends
C.They use black-box monitoring instead of exclusively white-box monitoring
D.Their dashboards refresh too slowly, at 30-second intervals instead of real time
Correct Answer: They monitor only known failure symptoms and lack leading indicators like saturation and error-rate trends
Explanation:
Reactive dashboards showing current status miss precursors. The four golden signals — latency, traffic, errors, and saturation — include leading indicators (rising saturation, creeping error rates) that predict failures before they manifest as downtime. Refresh rate and retention are not the core issue.
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54In Elasticsearch, a team indexes daily log indices but query latency degrades over months. Their oldest data is rarely queried. Which optimization directly addresses this?
Logging Fundamentals: ELK Stack (Elasticsearch, Logstash, Kibana)
Hard
A.Disable the Elasticsearch query cache to prevent stale results
B.Use Index Lifecycle Management to roll over and move old indices to a warm/cold tier and force-merge segments
C.Reindex all historical data into a single massive index for simpler querying
D.Increase the number of primary shards on every daily index to speed up all queries
Correct Answer: Use Index Lifecycle Management to roll over and move old indices to a warm/cold tier and force-merge segments
Explanation:
ILM automates hot-warm-cold transitions, force-merges rarely-changing indices into fewer segments, and moves them to cheaper, slower storage. This shrinks the hot working set and improves query latency. Over-sharding worsens overhead, and a single giant index removes pruning benefits.
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55An 'AIOps' platform correlates thousands of alerts into a single incident and suppresses the rest. During a novel failure mode, it wrongly suppresses the one alert that mattered. What is the key limitation illustrated?
Future of DevOps: GitOps, AIOps, NoOps
Hard
A.AIOps cannot ingest alerts from more than one monitoring source at a time
B.AIOps replaces all human on-call responders, so no one reviewed the alert
C.AIOps only works on metrics and is incapable of processing alert data
D.ML-based correlation depends on historical patterns and can misclassify or suppress genuinely novel signals
Correct Answer: ML-based correlation depends on historical patterns and can misclassify or suppress genuinely novel signals
Explanation:
AIOps models learn from past data and excel at reducing known noise, but novel failure modes fall outside their training distribution, risking incorrect suppression. This is why AIOps augments rather than replaces human judgment and needs feedback loops to handle the unknown.
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56A team practices trunk-based development but suffers frequent broken builds on the main branch. Which combination of practices most directly restores main-branch stability?
DevOps Best Practices
Hard
A.Committing directly to main and fixing breakages forward as they occur
B.Long-lived feature branches merged monthly after extensive manual QA
C.Short-lived branches with mandatory pre-merge CI checks and feature flags to decouple deploy from release
D.Disabling CI on pull requests to speed up the merge process
Correct Answer: Short-lived branches with mandatory pre-merge CI checks and feature flags to decouple deploy from release
Explanation:
Trunk-based development stays healthy when every change passes CI before merging and incomplete features hide behind flags rather than living on stale branches. Long-lived branches reintroduce merge hell, direct commits skip validation, and disabling CI removes the safety net entirely.
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57Google SRE distinguishes SLI, SLO, and SLA. A team promises customers 99.5% availability contractually but targets 99.9% internally and measures request success ratio. Which mapping is correct?
An SLI is the measured metric (success ratio), an SLO is the internal target for that SLI (99.9%), and an SLA is the externally-committed threshold with consequences (99.5%). Setting the SLO stricter than the SLA gives buffer before contractual penalties trigger.
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58Netflix's Chaos Monkey randomly terminates instances in production during business hours. What is the deliberate engineering rationale for this seemingly reckless timing?
Netflix Chaos Engineering
Hard
A.Business hours have the least traffic, so failures are cheapest then
B.It maximizes customer-visible downtime to justify larger infrastructure budgets
C.Production is the only environment where instances can be terminated at all
D.Engineers are present to observe and respond, forcing systems to be resilient to routine instance loss
Correct Answer: Engineers are present to observe and respond, forcing systems to be resilient to routine instance loss
Explanation:
Running during working hours ensures humans can watch, learn, and intervene, and it pressures teams to build services that tolerate instance death as a normal event. The goal is to make resilience a design default, not to cause downtime or exploit low traffic.
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59A team argues that metrics alone are sufficient because they can compute percentiles and rates cheaply. What fundamental limitation of pre-aggregated metrics does this overlook?
Observability: Metrics, Logs, Traces
Hard
A.Metrics are always more expensive to store than raw logs at any scale
B.Metrics cannot be visualized in dashboards without traces present
C.Metrics cannot represent latency, only counts of successful requests
D.Metrics lose per-event context, so you cannot ask arbitrary new questions after the data is aggregated
Correct Answer: Metrics lose per-event context, so you cannot ask arbitrary new questions after the data is aggregated
Explanation:
Aggregation discards individual event detail. Once data is rolled into counters and histograms, you cannot retroactively slice by a dimension you did not pre-define. Logs and traces preserve high-cardinality, per-event context needed for unanticipated investigations.
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60A startup claims to have achieved true 'NoOps' by adopting serverless. Which critique most accurately reflects the practical reality of NoOps?
Future of DevOps: GitOps, AIOps, NoOps
Hard
A.NoOps requires abandoning all monitoring since the provider guarantees uptime
B.Operational concerns like observability, cost governance, and failure handling still exist; they shift to developers and the provider rather than disappearing
C.NoOps is fully achievable and eliminates all operational responsibility once serverless is adopted
D.NoOps only applies to on-premises data centers and is impossible in the cloud
Correct Answer: Operational concerns like observability, cost governance, and failure handling still exist; they shift to developers and the provider rather than disappearing
Explanation:
NoOps abstracts away infrastructure management but does not eliminate operations. Responsibilities like monitoring, debugging, cost control, and handling provider failures move to developers and the platform. It is a shift in ownership, not the disappearance of ops work.
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