Question 1
Difficulty: medium
How do you decide what signals to instrument first when a new service is being launched?
Sample answer
I start by tying instrumentation to the business and technical risks of the service, not by trying to measure everything at once. First I identify the critical user journeys and the service-level objectives that matter most, then I map the request path to the points where failures, latency, or saturation are most likely to show up. For a new service, that usually means tracing around the main API calls, basic RED metrics for throughput, errors, and duration, plus a few resource indicators such as CPU, memory, queue depth, or dependency latency. I also make sure logs have enough structure to answer common debugging questions without forcing people to grep through noise. My goal is to create a minimal but useful baseline, then expand based on the incidents and questions that actually come up. That keeps the observability stack practical, avoids unnecessary cost, and helps the team build trust in the data early.
Question 2
Difficulty: medium
Tell me about a time you reduced monitoring noise without losing important visibility.
Sample answer
In a previous role, the team was getting overloaded with alerts from a large number of low-value checks, and people had started ignoring notifications altogether. I reviewed the alert history, grouped alerts by incident value, and found that many of them were symptoms rather than useful signals. I worked with service owners to separate page-worthy alerts from informational ones, then tightened the thresholds and added aggregation where appropriate. In a few cases, we replaced static thresholds with burn-rate alerts tied to SLOs, which made the alerts much more meaningful. I also helped standardize dashboard layouts so engineers could quickly identify whether a problem was service-specific or platform-wide. The biggest win was cultural as much as technical: once the team trusted that alerts were actionable, response times improved and there was less alert fatigue. I learned that reducing noise is not about hiding issues; it is about making signal quality high enough that people can act confidently.
Question 3
Difficulty: medium
How would you investigate a production latency spike when you only have dashboards, logs, and traces?
Sample answer
I’d approach it by narrowing the scope quickly and looking for where the latency is introduced rather than just where it is observed. I’d start with the dashboard to confirm whether the issue is isolated to one endpoint, one region, or one dependency, and whether the spike is tied to errors, traffic volume, or saturation. Then I’d use traces to see whether the latency is in the application itself, a database call, a downstream service, or a queue. Logs help confirm the behavior at the exact time the traces show the slowdown, especially if there are timeouts, retries, or pool exhaustion. If the problem is intermittent, I’d compare healthy and unhealthy requests to find a common pattern. I also like to ask whether anything changed recently, because deployments, config changes, and traffic shifts are often the real trigger. The key is to use all three signals together instead of treating them as separate tools.
Question 4
Difficulty: hard
What is your approach to designing SLIs and SLOs for a distributed system?
Sample answer
I start with the user experience and work backward. The best SLIs are simple, measurable, and directly related to what users care about, such as successful requests, latency for a critical endpoint, or freshness of data. For distributed systems, I try to avoid measures that are technically easy but not meaningful, because that can lead to false confidence. Once the right SLIs are defined, I set SLOs based on the actual reliability expectations of the product and the tradeoffs the business is willing to accept. I also make sure the error budget is usable, not just a number on a dashboard. That means agreeing on what happens when the budget is consumed, such as slowing feature releases or prioritizing reliability work. In practice, I work closely with product and engineering leaders so the SLOs are realistic and actionable. A good SLO program should help teams make decisions, not just report compliance.
Question 5
Difficulty: hard
Describe how you would roll out OpenTelemetry across multiple services in a company with mixed technology stacks.
Sample answer
I would treat it as a phased platform rollout, not a one-time instrumentation project. First I’d define the standard signals we want across the company: traces, metrics, logs, context propagation, and a consistent resource model. Then I’d pick a few representative services, ideally across different stacks, and use them as reference implementations. That lets us validate collector configuration, sampling strategy, attribute conventions, and backend compatibility before broad adoption. I would also document starter templates and language-specific examples so teams can move quickly without reinventing patterns. For mixed stacks, consistency matters more than perfection, so I’d focus on shared naming, propagation, and essential spans first, then expand to deeper instrumentation over time. I’d partner with service owners to make sure the effort improves their debugging experience, because adoption tends to stick when engineers see immediate value. The main risk in a rollout like this is fragmentation, so I would keep the standards lightweight and the migration path very practical.
Question 6
Difficulty: medium
How do you balance observability coverage with cost and performance overhead?
Sample answer
I treat observability as an engineering investment, so I always balance the value of the signal against its cost. The first step is understanding which services are critical and where outages or latency actually hurt the business. Those areas deserve richer telemetry and lower thresholds for visibility. For everything else, I look for efficient defaults and selective depth. For example, I might use high-cardinality data only where it is truly needed, apply sampling to traces, and retain detailed logs for shorter periods while keeping aggregated metrics longer. I also try to avoid instrumenting every code path equally; a few well-placed spans and metrics often reveal more than a flood of data. Performance overhead matters too, so I pay attention to the impact of logging volume, synchronous exporters, and expensive labels. Good observability should make the system easier to operate without becoming the thing that destabilizes it. I think the best approach is iterative: measure, review cost, adjust, and keep the signals that consistently help engineers resolve issues faster.
Question 7
Difficulty: medium
A service owner says they do not trust the monitoring data because it seems inconsistent. How would you handle that?
Sample answer
I would treat that as a signal that the observability implementation needs validation, not as resistance from the service owner. First I’d reproduce the inconsistency with them and compare what the dashboards, logs, and traces are showing versus the actual service behavior or known events. Often the issue comes down to a bad metric definition, missing tags, delayed ingestion, timezone confusion, or mismatched service naming. I’d trace the data path end to end, from instrumentation to collector to backend, to make sure the problem is not in the pipeline. If the data is technically correct but misleading, I’d improve the dashboard or the query so it matches how the service is actually used. I also think communication matters: I’d explain what the signal can and cannot tell us, and I’d make sure any fixes are visible so the owner can see progress. Trust in observability is earned through consistency, transparency, and fast follow-through.
Question 8
Difficulty: hard
How do you troubleshoot missing traces or gaps in distributed tracing across microservices?
Sample answer
I’d start by determining whether the issue is instrumentation, propagation, sampling, or export. If traces are missing only between certain services, I would check whether trace context is being passed correctly across HTTP, messaging, or async boundaries. For example, a missing header or an unsupported propagation format can break the chain even when each service is instrumented. If the spans are created but not visible in the backend, I’d inspect collector health, batching, queue saturation, and sampling configuration. I also look for mismatched service names or resource attributes, because that can make traces appear fragmented even when they are technically present. Another common issue is overaggressive sampling, which can create the illusion of missing traces when they are simply being dropped. My approach is to verify one request path end to end, then compare it with the failure cases. Once I know where the break happens, the fix is usually straightforward. The real skill is avoiding assumptions and following the trace lifecycle carefully.
Question 9
Difficulty: medium
Tell me about a time you influenced teams to adopt better observability practices without direct authority.
Sample answer
I worked on a platform where several teams were instrumenting services in different ways, which made it hard to compare incidents across systems. I didn’t have authority over those teams, so I focused on making the benefits obvious. I started by collecting examples where better traces or structured logs would have shortened debugging time, then I shared those findings in a practical way rather than a policy-driven one. I created lightweight templates for dashboards, alert rules, and service naming conventions, and I offered to pair with engineers during their next release. That helped people see the value without feeling like observability was being imposed on them. Over time, a few early adopters had faster incident resolution, and that became the proof point others wanted. What worked best was being helpful, responsive, and opinionated only where consistency truly mattered. I learned that standardization spreads more naturally when engineers feel it saves them time instead of adding process.
Question 10
Difficulty: hard
If leadership asked you to improve incident detection by next quarter, what would your plan look like?
Sample answer
I’d break the work into quick wins and structural improvements. First I’d analyze recent incidents to identify where detection failed: were alerts too late, too noisy, tied to the wrong metrics, or missing completely? That would tell me which systems need attention first. In the first phase, I’d focus on high-impact services and replace weak alerts with more meaningful ones, often using SLO-based alerting and better dashboard context. In parallel, I’d improve the signal quality by checking metric coverage, log structure, and trace propagation for those services. The second phase would be about repeatability: creating standards for alert severity, ownership, runbook links, and dashboard design so the improvements can scale. I’d also track a small set of metrics such as mean time to detect, alert precision, and incident recurrence. That gives leadership evidence that the changes are working. My goal would be to make detection faster without making the system noisier or more expensive than it needs to be.
