A candidate found four bad practice questions. Here is what we did about it Key takeaways A candidate flagged four ACP-120 practice questions. All four were bad, and all four had passed four rounds of review. Every one of those rounds read the question next to the Atlassian page. None of them ran anything.
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Build search parameters from raw values with URLSearchParams so a plus sign or ampersand in the user's input survives the round trip. A prototype may work with a single word and fail with a team name such as C++ & design. Concatenating that value into a query string gives punctuation another job: an ampersand can separate parameters, and a plus sign in form-style query syntax can be decoded as a space. Rehearse a value that contains both characters. Use a URL object for the full address, then set the unencoded input value on its searchParams.
🚀 Running AWS Strands Decider 2B Locally for Multi-RAG and Agentic AI Applications As GenAI applications become more sophisticated, one challenge continues to surface: How do we make reliable decisions before invoking an LLM? For example: Which datasource should answer this question? Should I query PLM, Jira, or Confluence? Do I have enough context to answer confidently? Should an AI agent invoke a tool or ask a clarification question?
Retry Storms: How a 502 Fix Created a Thousand Duplicate Orders In modern distributed systems, transient failures are inevitable. Timeouts, circuit breakers, and retries are standard defense mechanisms. But when idempotency—the guarantee that processing the same request multiple times has the same effect as processing it once—is skipped, retries turn into a destructive force. This isn’t a theoretical scenario. It’s a real incident that unfolded in a production e-commerce system, where a single upstream timeout cascaded into thousands of duplicate orders.
Now we move deeper into graphs. The key interview question is: Given a graph, how do I find the minimum-cost path from one node to another? The correct algorithm depends mainly on edge weights. Memorize this table: Graph Algorithm Unweighted graph BFS Weights are only 0 and 1 0-1 BFS Non-negative weights Dijkstra Negative weights possible Bellman-Ford All-pairs shortest paths Floyd-Warshall DAG Topological-order shortest path Unweighted → BFS Non-negative weighted → Dijkstra Negative edges → Bellman-Ford You already saw this with BFS. Example: 0 --- 1 --- 2 | 3 Every edge has equal cost.
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