Google Denies Gemini Ads Launch in 2026: What Marketers Need to Know
9 Dec 2025
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Performance & Scalability
Outline the difference between RTO (Recovery Time Objective) and RPO (Recovery Point Objective). For a critical financial application, propose an acceptable RTO and RPO and explain the architectural necessity for achieving them (e.g., multi-region deployment).
Outline the mandatory best practice for storing and displaying time-sensitive data (e.g., order history) in a global application. Specify the single Time Format (e.g., UTC) that must be used for storage in the database and the required conversion for display to the user.
Given a slow SQL query: SELECT * FROM users WHERE status = 'active' AND last_login < '2024-01-01', propose two distinct methods for optimization: 1. Indexing Strategy and 2. Query Refinement, explaining how each reduces load time.
Explain the concept of Application Caching (e.g., Redis). Define the two types of data that are ideal for caching (e.g., static content, expensive query results) and explain the risk of Stale Data and how to manage it (e.g., Time-To-Live/TTL).
Define Horizontal Scaling. For a stateless web API, list 3 architectural components (e.g., load balancer, auto-scaling group) required to enable the application to scale horizontally in response to sudden traffic spikes.
Explain the function and primary performance benefit of using a Content Delivery Network (CDN). Identify 3 types of application assets (e.g., images, JavaScript files) that should always be served via a CDN.
Describe the role of a Circuit Breaker Pattern in a microservices architecture. Explain the three primary states (Closed, Open, Half-Open) and how this pattern prevents cascading failures when a downstream service becomes unresponsive.
Design a Load Testing Scenario for a new e-commerce login page that needs to handle 5,000 concurrent users. Define the specific Thresholds for Failure (e.g., P95 Latency >500ms, Error Rate >1%) and the tool category needed for the test.
Identify two common programming practices (e.g., excessive object creation, unclosed resources) that lead to high Memory Usage and potential OutOfMemory (OOM) errors in an application. Propose a code review best practice to prevent this.
List 5 essential Infrastructure Metrics (e.g., CPU, RAM, Disk I/O) that an application operations team must monitor to proactively detect performance bottlenecks before they impact users.
Describe how a Message Queue (e.g., Kafka, RabbitMQ) and Rate Limiting can be used together to protect a vulnerable back-end service from being overwhelmed by spikes of inbound requests.
Develop a policy for monitoring and acting on application error logs. The policy must specify the minimum acceptable Error Rate (e.g., <0.1%) and the mandatory escalation path when the rate spikes above this threshold.
For a serverless function (e.g., AWS Lambda), identify the two primary factors that determine the cost (e.g., execution time, memory allocated). Propose one method for optimizing the application code to reduce the execution time and cost.
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News & Blog
