Interview Bank
328 questions total; 2 shown with current filters.
CourseAllFrom Frontend Engineer to Agent Engineer in 30 DaysPrompt Engineering From Scratch in 5 DaysMastering Claude: From Conversation to Claude Code in 5 DaysMastering Codex and the OpenAI Agents SDK in 5 DaysMCP in 7 Days: Wire Tools Into Any AgentAgent Skills in 7 Days: Turn Experience Into Reusable CapabilityContext Engineering in 5 DaysRAG in 14 Days: From Retrieval to Trustworthy AnswersBuild an AI Short-Drama Production Pipeline With Agents in 14 Days
Tag
All#pipeline-design2#cost14#evaluation14#reliability12#agent-skills11#architecture11#observability10#error-handling9#security9#api-design6#coding-agent6#debugging5
235 more tagsShow fewer tags
#idempotency5#streaming5#structured-output5#chunking4#deployment4#distributed-systems4#rag4#system-prompt4#tool-calling4#client3#embeddings3#failure-modes3#ingestion3#mcp3#message-bus3#operations3#progressive-disclosure3#ranking3#timeline3#agent-loop2#agentic-rag2#agents-sdk2#caching2#citations2#code-review2#communication2#concurrency2#consistency2#context2#context-engineering2#context-rot2#cost-control2#data-modeling2#grounding2#hybrid-search2#langgraph2#latency2#model-migration2#model-routing2#multi-agent2#ordering2#prompt-basics2#prompt-engineering2#protocol2#rate-limiting2#react2#redis-streams2#responses-api2#retrieval2#retrieval-quality2#routing2#runtime2#sse2#state-management2#statelessness2#subagents2#system-design2#tool-design2#tooling2#tracing2#trade-offs2#transport2#vector-database2#versioning2#workflow2#abstention1#access-control1#agent-design1#agent-quality1#altitude1#approvals1#async1#async-task1#atomicity1#attention-budget1#auth1#av-sync1#behavioral1#bm251#candidate-selection1#capacity-planning1#chain-of-thought1#checkpointing1#ci1#citation-verification1#claude-code1#cli-design1#cloud1#compaction1#compression1#content-hash1#context-compression1#context-window1#contextual-retrieval1#cost-optimization1#cross-model1#dag1#data-quality1#database1#decision-making1#decomposition1#degradation1#deliberate-practice1#design1#diagnostics1#dimensions1#distribution1#docker1#documentation1#engineering-judgement1#engineering-tradeoffs1#eval1#event-driven1#fallback1#fan-out1#ffmpeg1#forking1#four-elements1#framework-design1#framework-selection1#golden-set1#hallucination1#handoffs1#headless1#hnsw1#hybrid1#hyde1#image-generation1#incremental-recompute1#incremental-sync1#index-maintenance1#index-routing1#indexing1#information-retrieval1#instruction-hierarchy1#intent-routing1#interrupt-merge1#interview-prep1#invalidation1#isolation1#ivfflat1#just-in-time1#knowledge-organization1#lease1#llm-as-judge1#llm-output-quality1#long-context1#loop-guard1#media-pipeline1#metadata1#metrics1#mobile1#model-selection1#multi-tenancy1#multimodal1#nodejs1#orchestration1#pagination1#parent-child1#pdf-parsing1#performance1#permissions1#persistence1#pgvector1#pipeline-reliability1#portfolio1#prioritization1#production-readiness1#prompt-assembly1#prompt-caching1#prompt-injection1#prompt-limits1#prompt-techniques1#prompt-template1#prompt-versioning1#provider-abstraction1#quality-check1#quantization1#query-transformation1#quiet-hours1#rank-fusion1#reasoning1#recall1#redis1#reflection1#refusal1#reporting1#reproducibility1#rerank1#retrieval-failure1#retrieval-metrics1#retry1#retry-semantics1#retry-strategy1#review1#rollback1#rrf1#sandbox1#sandboxing1#scalability1#scheduling1#schema-design1#scoping1#scripts1#secrets-management1#self-assessment1#self-presentation1#self-reflection1#service-architecture1#session-management1#sessions1#sharding1#skill-authoring1#skill-description1#skills1#spec1#state-machine1#stateless1#stopping-criteria1#subtitles1#task-graph1#team-governance1#testing1#tool-budget1#tool-execution1#tool-naming1#tools1#tts1#tuning1#ux1#validation1#vector-index1#verification1#workflow-engine1#xml-tags1
Build an AI Short-Drama Production Pipeline With Agents in 14 Days
D1 What an AI Short-Drama Production Pipeline Looks Like: Breaking Down the Stages, a Task-Graph Architecture, and Choosing Among Four Categories of Generation Models
What does modeling a multi-step generation pipeline as a task graph buy you over a chain of sequential awaits, and what does it cost?把一条多步生成流程建成任务图,比一串顺序 await 多拿到了什么?代价是什么?
Common in ChinaCommon overseasIntermediate#task-graph#pipeline-designHow to reason about it · think before answering
- The question is what you gain, not what a DAG is. Reciting the definition scores nothing; name three capabilities the sequential version cannot have, each with a concrete scenario.
- Break it down by inverting the three pains of sequential code. First, parallelism is expressed by the graph itself — voice-over depends only on the lines, yet a sequential run queues it behind forty video jobs. Second, resumability — each node writes artifacts to a fixed path, so shot 37 failing does not destroy the first 36. Third, observability — you can say which node is stuck, not merely that some await is pending.
- Add the higher-signal point: cycle detection. Topological sort throws when dependencies form a cycle, and that is the only thing enforcing the acyclic part. Without it, a wrong dependency silently skips a step or reorders execution, which is painful to debug.
- Conclusion and cost: a task graph is not free. Every node needs a declared input and output artifact set, otherwise the graph is decorative. That artifact contract is also the precondition for idempotency and resume later on.
- Likely follow-up: should you adopt a workflow engine instead? Judge by node count and failure rate — worth it at a dozen-plus nodes with high failure and human review; for three to five nodes a hand-written graph plus topological sort is cheaper than another system to operate.
分析过程 · 先想清楚再作答
- 这题的题眼在「多拿到了什么」,不在「什么是 DAG」。背出有向无环图定义的人拿不到分,答对的人会给出三样顺序版拿不到的能力,并各配一个具体场景。
- 怎么拆:把顺序版的三个痛点倒过来说。第一,并行的可能性被图结构直接表达——配音只依赖台词、和画面无关,顺序版里它却要排在四十次视频生成后面。第二,有断点——每个节点的产物落在磁盘固定位置,第三十七个镜头失败时前三十六个还在。第三,可观测——你能回答「现在卡在哪个节点」,顺序版只能回答「卡在某个 await」。
- 补一条区分度更高的:环检测。拓扑排序在发现依赖成环时抛错,这是「无环」两个字唯一的执行者;没有它,依赖写错只会表现成漏跑一步或者顺序错乱,非常难查。
- 结论与代价:任务图不是免费的,你必须为每个节点定义清楚输入产物与输出产物,否则它只是一张漂亮的依赖声明。这份产物契约同时也是后面做幂等与断点续跑的前提。
- 可预期的追问:那是不是应该直接上工作流引擎?判据是节点数与失败率——十几个节点、失败率高、需要人工介入时才值得;三五个节点的流程用一张手写的图加拓扑排序就够,引入引擎反而多一套要运维的东西。
Key points
- Three things sequential code cannot give: parallelism expressed by structure, resumability after failure, and knowing which node is stuck
- Topological sort also detects cycles, the only mechanism enforcing the acyclic property
- The cost is declaring input and output artifacts per node; without that the graph is decorative
- That artifact contract is the precondition for idempotency and resume
- Adopt a workflow engine based on node count and failure rate; a hand-written graph wins for three to five nodes
答题要点
- 三样顺序版拿不到的:并行由图结构表达、失败后有断点、能说清卡在哪个节点
- 拓扑排序顺带做环检测,这是「有向无环」里「无环」的唯一执行者
- 代价是必须为每个节点声明输入产物与输出产物,否则图只是装饰
- 这份产物契约同时是后续做幂等与断点续跑的前提
- 上不上工作流引擎按节点数与失败率判断,三五个节点手写图更划算
D5 Voiceover, Subtitles, and Audio Tracks: Multi-Character Voices, Timeline Alignment, and Subtitle Files
When the synthesized speech and the shot duration disagree, which side do you adjust, and why?语音时长和画面时长对不上,你会调哪一边?为什么?
Common in ChinaCommon overseasIntermediate#timeline#tts#pipeline-designHow to reason about it · think before answering
- Answering 'stretch the shot' alone scores nothing; the hinge is 'why'. They want the reasoning for which side yields, and whether you see that this choice fixes the order of the whole pipeline.
- State the criterion: which distortion does the audience notice? Clipped or sped-up dialogue is audible immediately; a shot running 0.8 seconds long is not. So the picture yields.
- Derive the pipeline order from that: generate video at the planned duration, synthesize speech, write the measured duration back onto the shot, and let the editor pad the picture. Why not synthesize first and generate video to fit? Because video APIs expose discrete duration options — you cannot ask for exactly 6.34 seconds.
- Add the engineering detail that cannot be skipped: the timeline must use durations measured from the rendered files, never character-count estimates. Estimation error accumulates line by line, and by the tenth line the subtitles visibly race the picture.
- Then the exception, which earns points: if a shot has intrinsic rhythm — a beat cut, a transition, an action match — the picture cannot simply be stretched, and the right fix is a shorter line in the script. That is why stretched shots should be flagged for human review rather than silently rewritten.
- Expect the follow-up: can't you just nudge the speaking rate? You can, but it costs you — rate changes affect timbre and delivery, and they change duration again, turning a one-way flow into a loop. Make the lead-in and tail padding adjustable and spend that budget before touching the rate.
分析过程 · 先想清楚再作答
- 这题只答「调画面」拿不到分,题眼在「为什么」——面试官要的是让步理由,以及你有没有意识到这个选择会决定整条流水线的排列顺序。
- 先给判断依据:哪一边的失真观众察觉得到。台词被切掉、或者被加速到语气变形,观众立刻听得出来;一镜比原计划长零点八秒,观众感觉不到。所以让步的是画面。
- 由这条判断反推流水线顺序:画面先按计划时长生成,语音合成完之后由真实时长回写镜头时长,剪辑台再去补足画面。为什么不倒过来先合成语音再按语音时长生成视频?因为视频接口的时长是有限档位的,你没法要求它精确生成 6.34 秒。
- 补一条不能省的工程细节:写进时间轴的必须是从落盘文件量出来的真实时长,不能是字数估算。估算误差是逐句累加的,第一句差两百毫秒,第十句就差两秒,成片上表现为字幕跟画面赛跑。
- 再说例外,这是加分项:如果这一镜的画面本身有强节奏(比如卡点、转场、动作衔接),画面就不能被随意拉长,这时候要回头改剧本把台词写短,而不是硬拉画面。所以被顶长的镜头应该被标记出来交给人复核,而不是程序默默改掉。
- 可以预期的追问:那不能微调语速吗?可以,但语速是有代价的——语速改变会同时改变音质与情绪表现,而且它会反过来再改一次时长,等于把一个单向流程变成了循环。留一点余量的做法是给留白参数一个可调区间,先动留白再动语速。
Key points
- Stretch the picture: clipped or sped-up dialogue is instantly audible, while a fraction of a second of extra shot length is not
- That fixes the pipeline order: generate video at planned duration, synthesize speech, write measured duration back, pad in the edit
- You cannot invert it and generate video to match speech, because video APIs only expose discrete durations
- The timeline must use durations measured from rendered files; character-count estimates accumulate error line by line
- Shots with intrinsic rhythm are the exception, so flag stretched shots for human review instead of silently rewriting them
答题要点
- 调画面:台词被切或被加速观众立刻察觉,镜头长零点几秒观众感觉不到
- 由此定下流水线顺序:画面按计划时长生成,语音合成后回写真实时长,剪辑台补足画面
- 不能倒过来按语音时长生成视频,因为视频接口的时长只有有限档位
- 时间轴必须用落盘文件量出的真实时长,字数估算的误差会逐句累加
- 画面有强节奏的镜头是例外,这类冲突应标记出来交人复核而不是程序默默改掉