DevOps & SRE Abbreviations
Practice common DevOps and SRE abbreviations: SLO, SLI, SLA, MTTR, MTTD, DORA, CI/CD, IaC, GitOps, FinOps, and incident management terms.
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1 / 25
What is the difference between an SLI, SLO, and SLA?
SLI is the actual measurement (e.g., 99.7% uptime this month). SLO is the internal target (e.g., 99.9% uptime). SLA is the contractual agreement with customers (e.g., 99.5% uptime or we pay credits). SLOs should be stricter than SLAs.
2 / 25
What does MTTR stand for and why is it important?
MTTR (Mean Time To Recovery) measures how quickly your team can restore service after a failure. It's one of the four DORA metrics. A low MTTR indicates mature incident response processes and good observability.
3 / 25
What does 'IaC' stand for and what problem does it solve?
IaC (Infrastructure as Code) means your servers, networks, and cloud resources are defined in code files (Terraform, Pulumi, CloudFormation). This enables version control, repeatability, and eliminates 'works on my cloud' problems.
4 / 25
What does 'GitOps' mean in a DevOps context?
GitOps means your Git repository is the source of truth. When you want to change infrastructure or deploy code, you commit to Git — automated systems (like Flux or ArgoCD) detect the change and reconcile the actual state to match.
5 / 25
In PagerDuty incident management, what do ACK, SNOOZE, and RESOLVE mean?
In PagerDuty: ACK (Acknowledge) means you're aware and working on it; SNOOZE temporarily silences alerts for a set period (useful during maintenance); RESOLVE closes the incident and stops all alerts. These are standard on-call response actions.
6 / 25
During a code review for the new microservice deployment, Sarah from SRE sends this message to the team channel: 'We're seeing an increase in 5xx errors – let's investigate. The *E2E latency* is spiking and hitting 300ms, which is significantly above our SLO of 150ms.' Which of the following best describes what Sarah is referring to?
E2E latency (End-to-End Latency) specifically measures the duration of a request as it traverses the entire system – from user input to database response. Sarah is highlighting a performance issue that's exceeding their Service Level Objective (SLO), which focuses on availability and responsiveness. While 5xx errors are problematic, E2E latency provides context about *where* the problem originates, differentiating it from simply counting error rates.
7 / 25
During a Slack discussion about the recent performance issues with the new user authentication service, David (a Developer) posted: 'Just ran some tests and the API response time is consistently above 800ms. We're hitting our 500ms SLO!'. Maria from SRE replied: 'That's concerning – let's examine the TTFB.' Which of the following best explains what Maria meant in this context?
TTFB stands for 'Time To First Byte'. It's a crucial metric in performance monitoring and represents the delay between sending an HTTP request and receiving the first byte of data from the server. Developers often focus on overall response times, but understanding TTFB helps pinpoint bottlenecks within the network or server-side processing. The other options represent different aspects of API calls (request volume, data transfer, or server-side processing time) rather than the initial delay.
8 / 25
Ben is using 'database bottleneck' to describe the issue. While an SLA (option 4) outlines *what* is being measured and the target performance, a database bottleneck refers to a specific technical problem – limitations in the database's ability to handle requests – that's directly impacting service availability. Options 1 and 2 are broader concepts within DevOps; option 3 incorrectly identifies the SLA as the root cause.
9 / 25
B: He's concerned with potential overload on a single service, leading to instability, and is advocating for proactive fault tolerance mechanisms.
C: He's focused solely on the application code itself, suggesting a thorough review of the business logic.
D: He's questioning the accuracy of the monitoring dashboards and recommends adjusting alert thresholds.
Alex's comment highlights a critical aspect of SRE – preventing cascading failures. A circuit breaker pattern is designed to temporarily halt requests to a failing service, isolating it from the rest of the system and preventing overload. Option A is incorrect because circuit breakers address *fault tolerance*, not just throughput; options C and D are irrelevant to the core issue of potential systemic instability caused by a single point of failure. This reflects a key SRE principle: anticipating and mitigating failures proactively.
10 / 25
During a PR review for the deployment of a new payment processing microservice, Liam (DevOps) notes: 'The recent spike in error rates coincides with increased transaction volume. We're seeing a significant increase in 5xx errors and a corresponding rise in latency – impacting our user experience.' Considering this context, which option best describes Liam's primary concern?
Liam's comment highlights a correlation between increased load and system instability, indicating an issue with scalability or resource constraints. Option C accurately reflects his concern – he's recognizing the need for proactive measures to ensure the system can handle peak demand without performance degradation. The other options misinterpret Liam's focus; data corruption (A), code optimization (B) and adjusting thresholds (D) are secondary concerns in this scenario.
11 / 25
During a code review for the new microservice deployment, Sarah from SRE sends this message to the team channel: 'We're seeing an increase in 5xx errors – let's investigate. The *E2E latency* is spiking and hitting 300ms, which is significantly above our SLO of 150ms.' Which of the following best describes what Sarah is referring to?
E2E latency (End-to-End Latency) specifically measures the duration of a request as it traverses the entire system – from user input to database response. Sarah is highlighting a performance issue that's exceeding their Service Level Objective (SLO), which focuses on availability and responsiveness. While 5xx errors are problematic, E2E latency provides context about *where* the problem originates, differentiating it from simply counting error rates.
12 / 25
During a Slack discussion about the recent performance issues with the new user authentication service, David (a Developer) posted: 'Just ran some tests and the API response time is consistently above 800ms. We're hitting our 500ms SLO!'. Maria from SRE replied: 'That's concerning – let's examine the TTFB.' Which of the following best explains what Maria meant in this context?
TTFB stands for 'Time To First Byte'. It's a crucial metric in performance monitoring and represents the delay between sending an HTTP request and receiving the first byte of data from the server. Developers often focus on overall response times, but understanding TTFB helps pinpoint bottlenecks within the network or server-side processing. The other options represent different aspects of API calls (request volume, data transfer, or server-side processing time) rather than the initial delay.
13 / 25
Ben is using 'database bottleneck' to describe the issue. While an SLA (option 4) outlines *what* is being measured and the target performance, a database bottleneck refers to a specific technical problem – limitations in the database's ability to handle requests – that's directly impacting service availability. Options 1 and 2 are broader concepts within DevOps; option 3 incorrectly identifies the SLA as the root cause.
14 / 25
B: He's concerned with potential overload on a single service, leading to instability, and is advocating for proactive fault tolerance mechanisms.
C: He's focused solely on the application code itself, suggesting a thorough review of the business logic.
D: He's questioning the accuracy of the monitoring dashboards and recommends adjusting alert thresholds.
Alex's comment highlights a critical aspect of SRE – preventing cascading failures. A circuit breaker pattern is designed to temporarily halt requests to a failing service, isolating it from the rest of the system and preventing overload. Option A is incorrect because circuit breakers address *fault tolerance*, not just throughput; options C and D are irrelevant to the core issue of potential systemic instability caused by a single point of failure. This reflects a key SRE principle: anticipating and mitigating failures proactively.
15 / 25
During a PR review for the deployment of a new payment processing microservice, Liam (DevOps) notes: 'The recent spike in error rates coincides with increased transaction volume. We're seeing a significant increase in 5xx errors and a corresponding rise in latency – impacting our user experience.' Considering this context, which option best describes Liam's primary concern?
Liam's comment highlights a correlation between increased load and system instability, indicating an issue with scalability or resource constraints. Option C accurately reflects his concern – he's recognizing the need for proactive measures to ensure the system can handle peak demand without performance degradation. The other options misinterpret Liam's focus; data corruption (A), code optimization (B) and adjusting thresholds (D) are secondary concerns in this scenario.
16 / 25
During a code review for the new microservice deployment, Sarah from SRE sends this message to the team channel: 'We're seeing an increase in 5xx errors – let's investigate. The *E2E latency* is spiking and hitting 300ms, which is significantly above our SLO of 150ms.' Which of the following best describes what Sarah is referring to?
E2E latency (End-to-End Latency) specifically measures the duration of a request as it traverses the entire system – from user input to database response. Sarah is highlighting a performance issue that's exceeding their Service Level Objective (SLO), which focuses on availability and responsiveness. While 5xx errors are problematic, E2E latency provides context about *where* the problem originates, differentiating it from simply counting error rates.
17 / 25
During a Slack discussion about the recent performance issues with the new user authentication service, David (a Developer) posted: 'Just ran some tests and the API response time is consistently above 800ms. We're hitting our 500ms SLO!'. Maria from SRE replied: 'That's concerning – let's examine the TTFB.' Which of the following best explains what Maria meant in this context?
TTFB stands for 'Time To First Byte'. It's a crucial metric in performance monitoring and represents the delay between sending an HTTP request and receiving the first byte of data from the server. Developers often focus on overall response times, but understanding TTFB helps pinpoint bottlenecks within the network or server-side processing. The other options represent different aspects of API calls (request volume, data transfer, or server-side processing time) rather than the initial delay.
18 / 25
Ben is using 'database bottleneck' to describe the issue. While an SLA (option 4) outlines *what* is being measured and the target performance, a database bottleneck refers to a specific technical problem – limitations in the database's ability to handle requests – that's directly impacting service availability. Options 1 and 2 are broader concepts within DevOps; option 3 incorrectly identifies the SLA as the root cause.
19 / 25
B: He's concerned with potential overload on a single service, leading to instability, and is advocating for proactive fault tolerance mechanisms.
C: He's focused solely on the application code itself, suggesting a thorough review of the business logic.
D: He's questioning the accuracy of the monitoring dashboards and recommends adjusting alert thresholds.
Alex's comment highlights a critical aspect of SRE – preventing cascading failures. A circuit breaker pattern is designed to temporarily halt requests to a failing service, isolating it from the rest of the system and preventing overload. Option A is incorrect because circuit breakers address *fault tolerance*, not just throughput; options C and D are irrelevant to the core issue of potential systemic instability caused by a single point of failure. This reflects a key SRE principle: anticipating and mitigating failures proactively.
20 / 25
During a PR review for the deployment of a new payment processing microservice, Liam (DevOps) notes: 'The recent spike in error rates coincides with increased transaction volume. We're seeing a significant increase in 5xx errors and a corresponding rise in latency – impacting our user experience.' Considering this context, which option best describes Liam's primary concern?
Liam's comment highlights a correlation between increased load and system instability, indicating an issue with scalability or resource constraints. Option C accurately reflects his concern – he's recognizing the need for proactive measures to ensure the system can handle peak demand without performance degradation. The other options misinterpret Liam's focus; data corruption (A), code optimization (B) and adjusting thresholds (D) are secondary concerns in this scenario.
21 / 25
During a code review for the new microservice deployment, Sarah from SRE sends this message to the team channel: 'We're seeing an increase in 5xx errors – let's investigate. The *E2E latency* is spiking and hitting 300ms, which is significantly above our SLO of 150ms.' Which of the following best describes what Sarah is referring to?
E2E latency (End-to-End Latency) specifically measures the duration of a request as it traverses the entire system – from user input to database response. Sarah is highlighting a performance issue that's exceeding their Service Level Objective (SLO), which focuses on availability and responsiveness. While 5xx errors are problematic, E2E latency provides context about *where* the problem originates, differentiating it from simply counting error rates.
22 / 25
During a Slack discussion about the recent performance issues with the new user authentication service, David (a Developer) posted: 'Just ran some tests and the API response time is consistently above 800ms. We're hitting our 500ms SLO!'. Maria from SRE replied: 'That's concerning – let's examine the TTFB.' Which of the following best explains what Maria meant in this context?
TTFB stands for 'Time To First Byte'. It's a crucial metric in performance monitoring and represents the delay between sending an HTTP request and receiving the first byte of data from the server. Developers often focus on overall response times, but understanding TTFB helps pinpoint bottlenecks within the network or server-side processing. The other options represent different aspects of API calls (request volume, data transfer, or server-side processing time) rather than the initial delay.
23 / 25
Ben is using 'database bottleneck' to describe the issue. While an SLA (option 4) outlines *what* is being measured and the target performance, a database bottleneck refers to a specific technical problem – limitations in the database's ability to handle requests – that's directly impacting service availability. Options 1 and 2 are broader concepts within DevOps; option 3 incorrectly identifies the SLA as the root cause.
24 / 25
B: He's concerned with potential overload on a single service, leading to instability, and is advocating for proactive fault tolerance mechanisms.
C: He's focused solely on the application code itself, suggesting a thorough review of the business logic.
D: He's questioning the accuracy of the monitoring dashboards and recommends adjusting alert thresholds.
Alex's comment highlights a critical aspect of SRE – preventing cascading failures. A circuit breaker pattern is designed to temporarily halt requests to a failing service, isolating it from the rest of the system and preventing overload. Option A is incorrect because circuit breakers address *fault tolerance*, not just throughput; options C and D are irrelevant to the core issue of potential systemic instability caused by a single point of failure. This reflects a key SRE principle: anticipating and mitigating failures proactively.
25 / 25
During a PR review for the deployment of a new payment processing microservice, Liam (DevOps) notes: 'The recent spike in error rates coincides with increased transaction volume. We're seeing a significant increase in 5xx errors and a corresponding rise in latency – impacting our user experience.' Considering this context, which option best describes Liam's primary concern?
Liam's comment highlights a correlation between increased load and system instability, indicating an issue with scalability or resource constraints. Option C accurately reflects his concern – he's recognizing the need for proactive measures to ensure the system can handle peak demand without performance degradation. The other options misinterpret Liam's focus; data corruption (A), code optimization (B) and adjusting thresholds (D) are secondary concerns in this scenario.
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