\n <Description/>\n subResults => (\n <Result/>\n )\n </Result>\n <Pagination/>\n</Page>","roughness":1,"roundness":null,"seed":63846827,"strokeColor":"#1e1e1e","strokeStyle":"solid","strokeWidth":2,"text":"<Page>\n <SearchBar>\n <AutocompleteDropdown/>\n </SearchBar>\n <Results>\n result => (\n <Result>\n <Title/>\n <Description/>\n subResults => (\n <Result/>\n )\n </Result>\n <Pagination/>\n</Page>","textAlign":"left","type":"text","updated":1758547509810,"version":381,"versionNonce":319527691,"verticalAlign":"top","width":322.9398498535156,"x":2214.0373418893028,"y":383.82000140681157},{"angle":0,"autoResize":true,"backgroundColor":"transparent","boundElements":null,"containerId":null,"fillStyle":"solid","fontFamily":5,"fontSize":20,"frameId":null,"groupIds":[],"height":575,"id":"sVtfM1m_Z2VP0OxAEWzuo","index":"ac","isDeleted":false,"lineHeight":1.25,"link":null,"locked":false,"opacity":100,"originalText":"Data model:\nAutocomplete {\n id: string,\n text: string,\n type: 'suggestion' | 'history-item',\n timestamps: number,\n }\n\nResult {\n id: string,\n title: string,\n description: string,\n subResults: Results[],\n media: [{\n type: 'img' | 'video' | etc.,\n url: string,\n text: string,\n dimensions: {\n height: number,\n width: number,\n }\n }]\n}","roughness":1,"roundness":null,"seed":48420980,"strokeColor":"#1e1e1e","strokeStyle":"solid","strokeWidth":2,"text":"Data model:\nAutocomplete {\n id: string,\n text: string,\n type: 'suggestion' | 'history-item',\n timestamps: number,\n }\n\nResult {\n id: string,\n title: string,\n description: string,\n subResults: Results[],\n media: [{\n type: 'img' | 'video' | etc.,\n url: string,\n text: string,\n dimensions: {\n height: number,\n width: number,\n }\n }]\n}","textAlign":"left","type":"text","updated":1758712236937,"version":542,"versionNonce":1005055149,"verticalAlign":"top","width":352.19970703125,"x":1163.015625,"y":887.79296875},{"angle":0,"autoResize":true,"backgroundColor":"transparent","boundElements":null,"containerId":null,"fillStyle":"solid","fontFamily":5,"fontSize":20,"frameId":null,"groupIds":[],"height":400,"id":"0EIRarhaELvmtz7E-afQI","index":"ad","isDeleted":false,"lineHeight":1.25,"link":null,"locked":false,"opacity":100,"originalText":"Interface: \nGET /v1/autocompletes?query=string\n{\n items: Autocomplete[]\n}\n\nGET /v1/results?query=string&page_size=number&page_number=number\n{\n items: Result[],\n pagination: {\n page_number: number,\n page_size: number,\n total_number_of_pages: number,\n total_number_of_items: number,\n}\n","roughness":1,"roundness":null,"seed":1597452237,"strokeColor":"#1e1e1e","strokeStyle":"solid","strokeWidth":2,"text":"Interface: \nGET /v1/autocompletes?query=string\n{\n items: Autocomplete[]\n}\n\nGET /v1/results?query=string&page_size=number&page_number=number\n{\n items: Result[],\n pagination: {\n page_number: number,\n page_size: number,\n total_number_of_pages: number,\n total_number_of_items: number,\n}\n","textAlign":"left","type":"text","updated":1758712643083,"version":470,"versionNonce":1833875043,"verticalAlign":"top","width":689.9794921875,"x":1676.419879276814,"y":888.7739244695846}],"report":{"architectureDesign":{"feedback":"The component hierarchy was logical, separating the page into modular units like SearchBar, AutocompleteDropdown, and Results. The choice of TanStack Query for managing server state showed familiarity with modern React patterns.","improvements":"The diagram logic focused heavily on component nesting. A clearer depiction of the data flow—specifically how URL changes trigger data fetching and how state is shared between the search bar and result list without props drilling—would have strengthened the design.","stepTitle":"Architecture Design"},"dataModel":{"feedback":"The proposed models were well-structured. Distinguishing between 'suggestion' and 'history-item' types via a discriminator field and including media dimensions to prevent Cumulative Layout Shift (CLS) demonstrated attention to detail and performance.","improvements":"While the view model was sound, the candidate could have discussed normalization or how to handle different result entity types (e.g., a product vs. an article) more explicitly to ensure the system is extensible for future content types.","stepTitle":"Data Model"},"finalThoughts":{"feedback":"The candidate demonstrated a comprehensive understanding of frontend systems, from API integration to UX and accessibility. They navigated trade-offs well, particularly regarding caching and user privacy, and communicated clear reasoning throughout the interview.","improvements":"To reach a higher level, the candidate should deepen their knowledge of specific WAI-ARIA patterns for complex widgets (beyond basic roles) and favor standard HTTP mechanisms over custom, complex solutions for network load management.","stepTitle":"Final Thoughts"},"interfaceDefinition":{"feedback":"The API definition was clean and adhered to RESTful standards, properly including versioning, pagination parameters, and structured response objects for both autocomplete and results.","improvements":"The interface lacked support for advanced search features like filtering or sorting. Adding properties for filters (e.g., category, date range) would have made the design more realistic for a comprehensive search system.","stepTitle":"Interface Definition"},"optimizationsAndDeepDive":{"feedback":"The candidate covered a broad range of topics, including debouncing, prefetching, accessibility (screen readers, reduced motion), and loading states (shimmers). The decision to avoid IndexedDB due to implementation complexity was pragmatically argued.","improvements":"The proposal to use long-polling to dynamically adjust prefetch timeouts based on server load was over-engineered. Using standard HTTP headers (like Retry-After) would be a more standard approach. Additionally, the candidate lacked specific knowledge on ARIA attributes specifically for the complex 'combobox' pattern.","stepTitle":"Optimizations and Deep Dive"},"requirementsGathering":{"feedback":"The candidate successfully identified both functional and UI requirements, including autocomplete logic, history management based on authentication status, and appropriate metadata display. The usage of the whiteboard to visualize the wireframe was effective.","improvements":"The candidate could have explored non-functional requirements earlier, such as support for different languages, device types, or specific latency budgets, rather than waiting for the optimization section to address performance constraints.","stepTitle":"Requirements Gathering"}}},"data-sentry-element":"DemoInterviewView","data-sentry-component":"DemoPage","data-sentry-source-file":"page.tsx"}] </article></body></html>

5:["$","$L1d",null,{"interview":{"interviewId":"1","topic":"Our users expect instant, relevant results every time they search for something. We need a design that prioritizes clarity and helps users quickly find exactly what they're looking for, whether it's information, images, or shopping. The experience should feel seamless and intuitive, guiding them effortlessly to their next step.","title":"Demo interview example for the google search (frontend)","exportedAt":"2026-01-04T13:57:25.093Z","messages":[{"id":"j97ee5ja3tb077sg00z6v72kdd7r30p5","order":0,"sender":"user","mode":"interview","comment":"Let's start with the requirements. What should a first-time visitor see when they land on the page?"},{"id":"j97f9wrbkesphvga48yxj0r6jd7r295t","order":1,"sender":"ai","mode":"interview","comment":"For a search-focused page, users typically see a clean, centered search bar with the company logo above it, perhaps some quick links or recent searches if applicable, to encourage immediate interaction. What requirements do you have in mind for the initial view?"},{"id":"j9718r7r0apvd0834r92wvtggn7r3bza","order":2,"sender":"user","mode":"interview","comment":"Let's start with the search bar."},{"id":"j972y8029etqthr2krn78qb0td7r34wq","order":3,"sender":"ai","mode":"interview","comment":"Sounds good. What specific requirements do you have for the search bar in terms of functionality and user interface?"},{"id":"j97eesp4cwrre7zczx2phrd7ph7r3mzh","order":4,"sender":"user","mode":"interview","comment":"We should include autocomplete that activates after three characters and shows the user's search history."},{"id":"j97c8m5tfrk93590aw7zh3kvh17r2nt3","order":5,"sender":"ai","mode":"interview","comment":"Clear—autocomplete after three characters with search history integration will enhance usability. How do you envision handling privacy for the search history in the UI?"},{"id":"j975me5j6tcp4ka3atbbvrmm657r219q","order":6,"sender":"user","mode":"interview","comment":"For anonymous users, store history locally. For logged-in users, fetch it from the backend."},{"id":"j97dv4c83r4fqfpe8a6mbfrt9n7r339w","order":7,"sender":"ai","mode":"interview","comment":"Understood—that's a solid way to manage search history based on user authentication. What other key requirements come to mind for the overall search experience?"},{"id":"j9784srv4fr7pdj9sp1sdsbcws7r3qmd","order":8,"sender":"user","mode":"interview","comment":"Autocomplete should appear as a dropdown list."},{"id":"j975ba0td5vf6s78xf57a0vsys7r30fm","order":9,"sender":"ai","mode":"interview","comment":"Perfect, a dropdown list for autocomplete will keep it intuitive. Are there any other UI requirements for the search page?"},{"id":"j973zhyn21757jxyr2sk2x9c5s7r2e03","order":10,"sender":"user","mode":"interview","comment":"Results should be a paginated list with page info in the URL for deep linking. Each item should show the page metadata."},{"id":"j97ane42weprq0neqbr2nmbsfx7r2z5n","order":11,"sender":"ai","mode":"interview","comment":"Solid—pagination with URL params allows for easy sharing and navigation, and including metadata like title and snippet will inform users quickly. What metadata fields specifically should each result item display?"},{"id":"j97f6bapydbmzmvrc2pq6gb3057r2bwz","order":12,"sender":"user","mode":"interview","comment":"Title, description, subtitles when available, and an image."},{"id":"j973bv5hzvsrtgpxky9z51pqgd7r28t1","order":13,"sender":"ai","mode":"interview","comment":"That makes sense—title, description with subtitles where available, and images will provide a rich, visual preview. Now that we've outlined the core requirements, how would you structure the high-level architecture for this search interface?"},{"id":"j97eyq6nz16aqtk3ve0xapn5wn7r288d","order":14,"sender":"user","mode":"tutor","comment":"Where should we start with the high-level architecture?"},{"id":"j97cym7pn24aqzvj2f3rtvpnz97r3dcx","order":15,"sender":"ai","mode":"tutor","comment":"Great question—for frontend system design, we often begin by breaking down the UI into key components, like the search bar, autocomplete dropdown, and results list, then consider how they'll interact via state management. How would you identify the main components here?"},{"id":"j970gcxtjwjns0x3ph8vhgrdhn7r3z9z","order":16,"sender":"user","mode":"tutor","comment":"What do you think about my proposal for the starting page?"},{"id":"j97ftqy4x115kf3b4nhae6gwwd7r3gek","order":17,"sender":"ai","mode":"tutor","comment":"I like your proposition—visualizing the search bar with the autocomplete dropdown below it, plus the component tree, gives a clear starting structure for the layout and hierarchy. It effectively shows how the AutocompleteDropdown nests under SearchBar. One refinement: your labels have a couple of 'Autocomplete 3' duplicates—perhaps clarify those as sequential suggestions. How do you plan to handle the data flow between the SearchBar and the backend for triggering the autocomplete?"},{"id":"j97at84g8b0bzq0xtzs7emfncs7r3cr8","order":18,"sender":"user","mode":"tutor","comment":"The input should be debounced just enough to feel instant. Around 200-300ms seems right."},{"id":"j9716mb7f6sqawdcspeyennw9h7r3t67","order":19,"sender":"ai","mode":"tutor","comment":"Debouncing at 200-300ms strikes a good balance for perceived responsiveness without overwhelming the backend—smart choice for the autocomplete trigger. In your diagram, the flow from user input to retrieving data is clear, though on the frontend, we'd handle the debounce before firing API calls. How do you plan to implement this debounce logic in the SearchBar component?"},{"id":"j974fj1dwqxavhqtqhvjks1zpx7r2vh9","order":20,"sender":"user","mode":"tutor","comment":"Yes, that works."},{"id":"j97102qw223234xsdr0jgjkdw97r3yf4","order":21,"sender":"ai","mode":"tutor","comment":"Yes, that aligns well. To build on that, what specific mechanism would you use in the SearchBar component to achieve this debouncing, like a timer or a library?"},{"id":"j973zk7rzmtbgt3xka6vzr2bmh7r2dj8","order":22,"sender":"user","mode":"interview","comment":"The input should be debounced just enough to feel instant. Around 200-300ms seems right."},{"id":"j970a2gesbqfy6q4dp37ay7ph17r3r1t","order":23,"sender":"ai","mode":"interview","comment":"Good call on debouncing at 200-300ms—it'll maintain that instant feel without overwhelming the backend. With the component structure like wrapping and , how do you see the interactions flowing between frontend and backend for autocomplete suggestions?"},{"id":"j97djth73a9yxh8hdxyetzce797r3a7n","order":24,"sender":"user","mode":"interview","comment":"AutocompleteDropdown should receive the debounced value from SearchBar and fetch from the backend. TanStack Query fits well."},{"id":"j97dwj9ret3d28wyr1ajyndzq97r3bsn","order":25,"sender":"ai","mode":"interview","comment":"That aligns well—using TanStack Query for fetching autocomplete data will handle caching and loading states efficiently on the frontend. How would you integrate the paginated results list into this architecture?"},{"id":"j97bzbrbhbv9rcqz0k3zxqqx717r2mqw","order":26,"sender":"user","mode":"interview","comment":"Here's an example of the integration."},{"id":"j971b8xegxrfg6a8epa5q0d5057r3w99","order":27,"sender":"ai","mode":"interview","comment":"That's a clear example of integrating the paginated results with nested sub-results where needed—it builds nicely on the page structure. Now that we've outlined the high-level architecture with components like SearchBar, AutocompleteDropdown, Results, and Pagination, can you describe the core data models for entities like search suggestions and result items?"},{"id":"j9776szbvg6ejk33f0rb1dt2vh7r691q","order":28,"sender":"user","mode":"tutor","comment":"How can I improve those data models?"},{"id":"j970bvthf3jvcpyftswbkwgezd7r6dqy","order":29,"sender":"ai","mode":"tutor","comment":"Your data models are a strong start—Autocomplete distinguishes between suggestion and history types effectively for UI rendering, and Result's recursive subResults and flexible media array support nested content well. To improve, consider adding fields like 'timestamp' to Autocomplete for sorting history by recency, and 'altText' or 'dimensions' to media for better accessibility and image handling. For Result, include an optional 'url' for direct linking. These enhance frontend rendering without overcomplicating. What do you think about incorporating loading or error states into the models for UI resilience?"},{"id":"j9773dewhxxw0qa0sy5bqn0rts7r7w8n","order":30,"sender":"user","mode":"interview","comment":"Here's my proposal for those data models."},{"id":"j97crstbrw5zjsqp5662vt3rdx7r6229","order":31,"sender":"ai","mode":"interview","comment":"That's a thoughtful data model—using types to distinguish suggestions from history, and including media dimensions will help with rendering flexibility. With these core entities defined, how would you define the API interfaces for fetching autocomplete suggestions and search results?"},{"id":"j97147w8efmhkkxzrbsnhtbwvn7r7fbg","order":32,"sender":"user","mode":"interview","comment":"V1 is the API version. We should always include a version in the API."},{"id":"j971ev4bt60hvmm70qkyca0be97r7zzk","order":33,"sender":"ai","mode":"interview","comment":"Good—incorporating API versioning from the start is a best practice for evolution without breaking changes. With the interfaces defined for autocompletes and paginated results, let's dive into optimizations: What key performance aspects would you focus on for delivering instant, relevant results?"},{"id":"j97c8s7tbwaymz9an55bzfajk57r6cs0","order":34,"sender":"user","mode":"interview","comment":"After the user stops typing, we could prefetch results a few seconds later. There are performance implications, so we need to be careful not to overload the server."},{"id":"j9715fqd9xyf8zvbscrqnwcfqn7r7jw6","order":35,"sender":"ai","mode":"interview","comment":"Prefetching results after the debounce period is a smart proactive optimization for perceived speed, and Yes, that works. balancing it to avoid server strain is crucial. How would you mitigate the potential overload from prefetching?"},{"id":"j970w1yzsd47j30gwytktrf8m57r64fs","order":36,"sender":"user","mode":"interview","comment":"The timing could be dynamic, adjusted based on server load."},{"id":"j97et7gjt60ps9czrm3sf22w2s7r6bnm","order":37,"sender":"ai","mode":"interview","comment":"Adjusting the prefetch timing dynamically based on server load is a solid adaptive strategy to prevent overload. How would you implement the mechanism to detect and respond to load changes in real-time?"},{"id":"j97eyn9nwd3731rrrp6w5rxx1d7r68sz","order":38,"sender":"user","mode":"interview","comment":"We could use long polling so the server can notify the frontend with a new timeout when load changes."},{"id":"j9773qpshmp7pvdt9x9ne21fjd7r7y1n","order":39,"sender":"ai","mode":"interview","comment":"Using long polling for the server to notify the frontend of adjusted timeouts based on load is an effective way for real-time adaptation. How would you handle the fallback if long polling fails or times out?"},{"id":"j972258cjc8q5z9zttcqhx7aw17r7tw3","order":40,"sender":"user","mode":"interview","comment":"In that case, we could turn off prefetching."},{"id":"j9753p69n7y2t26a796mrg0cyh7r6pr6","order":41,"sender":"ai","mode":"interview","comment":"Switching off prefetching as a fallback when long polling fails is a pragmatic safety net to maintain stability. What other frontend optimizations, like caching or rendering, would you prioritize for the search results to ensure seamless performance?"},{"id":"j97eyrn9d7x3gkb2x3mtbbsbn57r7vzq","order":42,"sender":"user","mode":"interview","comment":"Autocomplete suggestions and results could be cached on the server in a key-value store like Redis. We could also cache on the client in IndexedDB, but browser support varies."},{"id":"j9787wbrze3fry3tbfd8sztq1x7r6cz1","order":43,"sender":"ai","mode":"interview","comment":"Server-side caching with Redis will scale well for shared data, and exploring IndexedDB for client-side while addressing browser variances is thoughtful. How would you handle graceful degradation for browsers that don't support IndexedDB?"},{"id":"j97bhszfmgfgxqhez7e02qkfan7r65dx","order":44,"sender":"user","mode":"interview","comment":"Local storage is too small, so we'd disable local caching. IndexedDB also has issues, even supported browsers can be flaky and only one tab can connect, so it adds too much complexity."},{"id":"j97d6e04597kjdqq5w85p48pnx7r640v","order":45,"sender":"ai","mode":"interview","comment":"Switching off client-side caching due to size limits and IndexedDB complexities is a reasonable decision to avoid unreliable implementations. How would you ensure data freshness in the server-side Redis cache for autocomplete and results?"},{"id":"j970k9grspt2wf24vpc42ehyw97r7e0j","order":46,"sender":"user","mode":"interview","comment":"Eviction should consider when it was last used, how often it's used, and how old the result is."},{"id":"j9713z9ns1hjx5qp8vkxrx2s0x7r7dra","order":47,"sender":"ai","mode":"interview","comment":"Combining recency, frequency of use, and age for cache eviction sounds like an effective LRU-like strategy tailored to search patterns. How would you configure the TTL or eviction policies in Redis to balance freshness and performance?"},{"id":"j977h72mzzpzbaycjg4kk8d7tn7r6sdm","order":48,"sender":"user","mode":"interview","comment":"I don't have an answer for that, and it's out of scope for a frontend interview."},{"id":"j974rjzhrsejctbes7pejnvctx7r74g4","order":49,"sender":"ai","mode":"interview","comment":"Fair point—that's more backend territory, and we're focusing on frontend here. To wrap up optimizations, how would you handle loading states and error handling in the UI for autocomplete and results to keep the experience seamless?"},{"id":"j970jthpb13wxy9p9g38bqtsw97r7t50","order":50,"sender":"user","mode":"interview","comment":"Using TanStack Query makes sense since it handles loading and errors. For UX, show shimmering placeholders on the results page while loading and surface errors with a toast."},{"id":"j976q70tthrqz3g4105hfphx1d7r6xbp","order":51,"sender":"ai","mode":"interview","comment":"Leveraging TanStack Query for built-in loading and error management, paired with shimmering for results and toasts for notifications, will maintain that seamless feel. How would you implement the shimmering effect specifically for the paginated results list?"},{"id":"j97975b13mz7qnkptmaj8x2fy97r6831","order":52,"sender":"user","mode":"interview","comment":"We'd use div containers with a CSS shimmer animation, a list of placeholders roughly the size of typical results."},{"id":"j97ar6zjbjjc40qj9hj4k13ken7r71qx","order":53,"sender":"ai","mode":"interview","comment":"Excellent—using CSS animations on divs mimicking result sizes will provide smooth visual feedback during loading. How would you ensure this shimmering effect doesn't negatively impact users with motion sensitivity or reduced performance devices?"},{"id":"j97fq8km82k8pvqe94j7rf0dhs7r6m7t","order":54,"sender":"user","mode":"interview","comment":"We'd respect the prefers-reduced-motion media query."},{"id":"j97ae1ztzdqa50xbb4v8wafnp97r7033","order":55,"sender":"ai","mode":"interview","comment":"That's considerate—using prefers-reduced-motion media queries will respect accessibility preferences and improve usability. How would you test the overall search interface for responsiveness across different devices and network conditions?"},{"id":"j977ae4yyscfg0hg0y90p8m9bn7r689p","order":56,"sender":"user","mode":"interview","comment":"Chrome DevTools can simulate different CPU and network conditions."},{"id":"j97fnwrjksf3yyrns08cgas54n7r74am","order":57,"sender":"ai","mode":"interview","comment":"Using Chrome DevTools to simulate CPU throttling and network speeds is a reliable method for uncovering responsiveness issues across scenarios."},{"id":"j978qncthpss4zg070sbazt7cn7r6hgb","order":58,"sender":"user","mode":"interview","comment":"One thing left is accessibility."},{"id":"j9772hyyjascch3fs4z052k7dx7r6vk4","order":59,"sender":"ai","mode":"interview","comment":"Accessibility is crucial for an inclusive search experience. What key accessibility features would you prioritize in the UI for the search bar and results?"},{"id":"j97c8hrate0ddtd9trvt388z8h7r6d70","order":60,"sender":"user","mode":"interview","comment":"We should meet text contrast guidelines, smaller text needs higher contrast, and support screen readers with appropriate ARIA attributes."},{"id":"j97afcbd0gm7dqs8f1b632b58h7r7089","order":61,"sender":"ai","mode":"interview","comment":"Strong foundation—adhering to WCAG contrast ratios based on text size and implementing ARIA attributes for screen readers will make it more accessible. What specific ARIA roles or labels would you apply to the search bar and autocomplete dropdown?"},{"id":"j97ahwmsg17zgd10n2g18mrbyd7r7baz","order":62,"sender":"user","mode":"tutor","comment":"Which ARIA attributes should I use?"},{"id":"j97031wr0cap4gzfxpmajnbhws7r6t2f","order":63,"sender":"ai","mode":"tutor","comment":"For the search bar, use role='search' or role='combobox' on the input to indicate it's a search field with autocomplete, plus aria-label='Search' for screen readers if there's no visible label. For the autocomplete dropdown, set role='listbox' on the container, aria-expanded='false/true' to show open state, and on each option, role='option' with aria-selected for the focused item. This follows ARIA patterns for combobox widgets, ensuring navigable keyboard support and screen reader announcements. How would you integrate keyboard navigation with these attributes?"},{"id":"j97a9ftk2c2xvkggwzbhmenka17r6m3f","order":64,"sender":"user","mode":"interview","comment":"For the search bar I'd use role='search' with aria-label='Search', and for results a semantic

elements for better announcement of selections?"},{"id":"j975g8n6j1qv9h4fv9vz7q0tzn7r63w2","order":68,"sender":"user","mode":"tutor","comment":"How should we answer that?"},{"id":"j977jt081w37k73mw8vnnsn08h7r6xmc","order":69,"sender":"ai","mode":"tutor","comment":"For the autocomplete
- gets role='option', with aria-selected='false/true' on the highlighted one, and use aria-activedescendant on the input pointing to the current option ID for focus announcements. This creates an accessible combobox pattern where screen readers announce 'X of Y' and selections dynamically. Now, how would you test these ARIA implementations with tools like screen readers or auditors?"},{"id":"j9795cmwrsvpa3xm8yskewm7497r6gxf","order":70,"sender":"user","mode":"interview","comment":"I'd need to check the documentation to answer that. Is there anything else you'd like to discuss?"},{"id":"j97677kw5v6nqjtx246h1djv457r63bv","order":71,"sender":"ai","mode":"interview","comment":"Understood—ARIA patterns like autocomplete can be detailed, so that's fine. To conclude the interview, what trade-offs did you consider in prioritizing instant results over other aspects like personalization?"},{"id":"j97b55725wqhyrcnzmnvxvsams7r7wkw","order":72,"sender":"user","mode":"interview","comment":"For anonymous users we should prioritize instant results. Logged-in users may tolerate more delay, so personalization becomes more important."},{"id":"j97690bt2d806d13b8bsek5wjx7r6xan","order":73,"sender":"ai","mode":"interview","comment":"That's a nuanced trade-off—prioritizing speed for anonymous users while layering in personalization for logged-in ones based on expected patience levels. Thanks for walking through this design. You've covered the key aspects thoughtfully."},{"id":"j9763syahc6w15n1a1kcfptgdh7t51m6","order":74,"sender":"user","mode":"interview","comment":"Thanks for the interview."},{"id":"j97ftbqdqjkve0fzn09eb6055d7t4tgh","order":75,"sender":"ai","mode":"interview","comment":"You're welcome! It was a productive discussion."}],"snapshot":[{"angle":0,"autoResize":true,"backgroundColor":"transparent","boundElements":null,"containerId":null,"fillStyle":"solid","fontFamily":5,"fontSize":20,"frameId":null,"groupIds":[],"height":125,"id":"BqfwTxJWciDYL1MsbrlrK","index":"a0","isDeleted":false,"lineHeight":1.25,"link":null,"locked":false,"opacity":100,"originalText":"Requirements:\n- a prominent search bar\n- a dropdown autocomplete with user history in a form of a dropdown\n- autocomplete kicks in after 3 characters\n","roughness":1,"roundness":null,"seed":828876356,"strokeColor":"#1e1e1e","strokeStyle":"solid","strokeWidth":2,"text":"Requirements:\n- a prominent search bar\n- a dropdown autocomplete with user history in a form of a dropdown\n- autocomplete kicks in after 3 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/v1/autocompletes?query=string\n{\n items: Autocomplete[]\n}\n\nGET /v1/results?query=string&page_size=number&page_number=number\n{\n items: Result[],\n pagination: {\n page_number: number,\n page_size: number,\n total_number_of_pages: number,\n total_number_of_items: number,\n}\n","roughness":1,"roundness":null,"seed":1597452237,"strokeColor":"#1e1e1e","strokeStyle":"solid","strokeWidth":2,"text":"Interface: \nGET /v1/autocompletes?query=string\n{\n items: Autocomplete[]\n}\n\nGET /v1/results?query=string&page_size=number&page_number=number\n{\n items: Result[],\n pagination: {\n page_number: number,\n page_size: number,\n total_number_of_pages: number,\n total_number_of_items: number,\n}\n","textAlign":"left","type":"text","updated":1758712643083,"version":470,"versionNonce":1833875043,"verticalAlign":"top","width":689.9794921875,"x":1676.419879276814,"y":888.7739244695846}],"report":{"architectureDesign":{"feedback":"The component hierarchy was logical, separating the page into modular units like SearchBar, AutocompleteDropdown, and Results. The choice of TanStack Query for managing server state showed familiarity with modern React patterns.","improvements":"The diagram logic focused heavily on component nesting. A clearer depiction of the data flow—specifically how URL changes trigger data fetching and how state is shared between the search bar and result list without props drilling—would have strengthened the design.","stepTitle":"Architecture Design"},"dataModel":{"feedback":"The proposed models were well-structured. Distinguishing between 'suggestion' and 'history-item' types via a discriminator field and including media dimensions to prevent Cumulative Layout Shift (CLS) demonstrated attention to detail and performance.","improvements":"While the view model was sound, the candidate could have discussed normalization or how to handle different result entity types (e.g., a product vs. an article) more explicitly to ensure the system is extensible for future content types.","stepTitle":"Data Model"},"finalThoughts":{"feedback":"The candidate demonstrated a comprehensive understanding of frontend systems, from API integration to UX and accessibility. They navigated trade-offs well, particularly regarding caching and user privacy, and communicated clear reasoning throughout the interview.","improvements":"To reach a higher level, the candidate should deepen their knowledge of specific WAI-ARIA patterns for complex widgets (beyond basic roles) and favor standard HTTP mechanisms over custom, complex solutions for network load management.","stepTitle":"Final Thoughts"},"interfaceDefinition":{"feedback":"The API definition was clean and adhered to RESTful standards, properly including versioning, pagination parameters, and structured response objects for both autocomplete and results.","improvements":"The interface lacked support for advanced search features like filtering or sorting. Adding properties for filters (e.g., category, date range) would have made the design more realistic for a comprehensive search system.","stepTitle":"Interface Definition"},"optimizationsAndDeepDive":{"feedback":"The candidate covered a broad range of topics, including debouncing, prefetching, accessibility (screen readers, reduced motion), and loading states (shimmers). The decision to avoid IndexedDB due to implementation complexity was pragmatically argued.","improvements":"The proposal to use long-polling to dynamically adjust prefetch timeouts based on server load was over-engineered. Using standard HTTP headers (like Retry-After) would be a more standard approach. Additionally, the candidate lacked specific knowledge on ARIA attributes specifically for the complex 'combobox' pattern.","stepTitle":"Optimizations and Deep Dive"},"requirementsGathering":{"feedback":"The candidate successfully identified both functional and UI requirements, including autocomplete logic, history management based on authentication status, and appropriate metadata display. The usage of the whiteboard to visualize the wireframe was effective.","improvements":"The candidate could have explored non-functional requirements earlier, such as support for different languages, device types, or specific latency budgets, rather than waiting for the optimization section to address performance constraints.","stepTitle":"Requirements Gathering"}}},"data-sentry-element":"DemoInterviewView","data-sentry-component":"DemoPage","data-sentry-source-file":"page.tsx"}] </article></body></html>