Copy<\/button><\/div>\n\nimport requests\ndef fetch_all_records(base_url: str, params: dict) -> list[dict]:\n\n records = []\n\n page = 1\n\n while True:\n\n resp = requests.get(base_url, params={**params, \u201cpage\u201d: page})\n\n resp.raise_for_status()\n\n data = resp.json()\n\n batch = data.get(\u201cresults\u201d, [])\n\n records.extend(batch)\n\n if not data.get(\u201cnext\u201d): # None or empty string means no next page\n\n break\n\n page += 1\n\n return records\n<\/code><\/pre><\/div>Parse a customer-provided JSON configuration file and report every missing or malformed required field<\/span>FDSE \/ Implementation<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
Define the required schema as a simple spec: field name, expected type, and whether it’s required. Load the JSON, walk the spec, and for each required field check presence first, then type. Collect every violation rather than stopping at the first one, since a customer fixing a config file wants the full list of problems in one pass, not one error at a time.<\/p>\n
This is a direct proxy for the FDSE job: customers hand over configuration files with typos, wrong types, and missing fields on a regular basis, and a deployed engineer needs to give useful feedback fast rather than a stack trace. Reported from an FDSE-track phone screen in 2025. The interviewer’s follow-up asked for nested field validation, a “contact” object with its own required sub-fields, which means the validator needs to recurse rather than only check the top level.<\/p>\n
python<\/span>Copy<\/button><\/div>\n\ndef validate_config(config: dict, schema: dict, path: str = \u201c\u201d) -> list[str]:\n\n errors = []\n\n for field, spec in schema.items():\n\n full_path = f\u201d{path}.{field}\u201d if path else field\n\n if field not in config:\n\n if spec.get(\u201crequired\u201d, True):\n\n errors.append(f\u201dmissing required field: {full_path}\u201d)\n\n continue\n\n value = config[field]\n\n expected_type = spec[\u201dtype\u201d]\n\n if expected_type == \u201cobject\u201d:\n\n if not isinstance(value, dict):\n\n errors.append(f\u201d{full_path} should be an object\u201d)\n\n else:\n\n errors.extend(validate_config(value, spec[\u201dfields\u201d], full_path))\n\n elif not isinstance(value, expected_type):\n\n errors.append(f\u201d{full_path} should be {expected_type.__name__}\u201d)\n\n return errors\n<\/code><\/pre><\/div>Give an example of when you disagreed with a technical decision and how you handled it.<\/span>Behavioral<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
Pick a decision where you were genuinely wrong, or genuinely right but couldn’t get traction. Both are interesting. The pattern Palantir rewards: you brought a clear argument with evidence, you listened to the counterargument, and you either updated your position or escalated appropriately (not passive-aggressively). What they don’t want: either total deference (“I just went with what the team decided”) or stubbornness (“I implemented my own approach anyway”).<\/p>\n
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Tell me about a time you had to become productive in a codebase or domain you knew nothing about.<\/span>Behavioral<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
This maps directly to the Learning round and to the day-to-day of an FDSE embedded at a new customer site. Interviewers want a real account of how you oriented yourself, what you read first, who you asked, and how long it took before you shipped something real. Vague answers, “I just figured it out”, don’t land as well as a specific sequence: what you did in the first hour, the first day, the first week.<\/p>\n
Strong answers also name what you got wrong initially. Assuming you fully understood a system after a quick skim and then breaking something is a better story, honestly told, than claiming a clean ramp-up.<\/p>\n
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How long does the Palantir interview process take?<\/span>FAQ<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
Most candidates report 4 to 8 weeks from first contact to verbal offer. The OA and phone screen move quickly once the recruiter call is done, usually within a week each. The gap is typically between the onsite and the hiring manager final, which can take 1 to 2 weeks. Palantir does not move at Google or Amazon speed; they interview fewer candidates more deliberately.<\/p>\n
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Do I need to know Palantir's products (Foundry, Gotham, AIP) before the interview?<\/span>FAQ<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
Yes, at minimum for the recruiter screen. You don’t need product expertise, but you should know what Foundry is (Palantir’s commercial operating system, used by companies like Airbus and Ferrari for data operations), what Gotham is (the government and defense platform, used by intelligence agencies and military), and what AIP is (the AI layer announced in 2023 that sits on top of both). Candidates who can’t distinguish these during the “why Palantir?” question don’t advance. It takes 30 minutes of reading to get there.<\/p>\n
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What makes the Palantir Decomposition round different from system design?<\/span>FAQ<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
System design asks you to architect a technical system, scaling, storage, services, APIs. Decomposition asks you to figure out what the system should be before designing it. You’re given an ambiguous problem, no clear scope, no defined users, no stated constraints, and your job is to ask questions that reveal those things. No code is expected. The round rewards structured thinking about stakeholders, data, decisions, and MVP scope. Most candidates who fail it do so by jumping to technical architecture before they’ve defined the problem.<\/p>\n
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Is the Palantir interview harder than Google or Meta?<\/span>FAQ<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
Different in kind, not just in difficulty. Google and Meta run standardized loops where you can prep a specific set of patterns and expect consistent questions. Palantir’s loop varies by role (SWE vs. FDSE), by what rounds you’re assigned, and by how aggressively your behavioral fit is probed. Candidates who are strong at LeetCode sometimes fail at Palantir because the Decomposition round has no equivalent in standard prep. The coding rounds are LC Medium on average, which is not harder than Google or Meta. The behavioral bar is stricter.<\/p>\n
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What is a Forward Deployed Software Engineer at Palantir and how does the interview differ?<\/span>FAQ<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
FDSEs embed directly at customer sites (hospitals, defense agencies, financial institutions) and build software for those customers with minimal handholding. The role is part engineer, part consultant. The interview for FDSE skews heavier toward Decomposition and Learning rounds, which test how quickly you can orient in an unfamiliar environment and structure a solution for a non-technical stakeholder. DSA questions are lighter. Behavioral questions about working with ambiguous requirements and non-technical clients are heavier. You’re also asked directly: “why FDSE specifically and not SWE?”<\/p>\n
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Does Palantir offer return offers to interns? What does the intern interview look like?<\/span>FAQ<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
Palantir has a reputation for a high intern-to-return-offer conversion rate for candidates who perform well during the internship, though the company has also had hiring freezes that affected internship conversion in 2023 and 2024. The intern interview includes the OA and a shorter onsite (typically 2 to 3 rounds rather than 4 to 5). Decomposition is usually included even for interns. The behavioral bar for interns is lower on “why Palantir” but the company still filters for genuine interest over generic tech-job motivation.<\/p>\n
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Are there product sense or case-style questions in the Palantir software engineering loop?<\/span>FAQ<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
Not as a separate labeled round, but the Decomposition round functions like one. Instead of “how would you improve Instagram Stories,” you get an operational problem, hospital bed allocation, disaster response coordination, and are evaluated on how you scope it, not on writing code. Data modeling shows up too, both in Decomposition and occasionally as its own System Design question centered on Palantir’s ontology concept, the object layer that links related entities across otherwise disconnected datasets. If you’re prepping only DSA and system design in the traditional sense, you’re missing this entire axis of the loop.<\/p>\n
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What is a hash table, and how does it achieve average O(1) lookups?<\/span>Hash Tables<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
A hash table stores key-value pairs by running each key through a hash function that produces an integer, then using that integer (modulo the table size) as an index into an underlying array. Insert, lookup, and delete all reduce to computing the hash once and jumping straight to the right bucket, so average case time doesn’t depend on how many other entries are in the table.<\/p>\n
That average case falls apart if the hash function clusters many keys into the same bucket. With a bad hash function, or an adversary who crafts keys to collide on purpose, a bucket can end up holding most of the entries, and lookups in that bucket degrade to a linear scan, O(n) in the worst case. Real implementations guard against this by resizing and rehashing once the load factor (entries divided by bucket count) crosses a threshold, usually around 0.75, and by chaining collisions into small linked lists or trees (Java’s HashMap switches a bucket from a linked list to a red-black tree once it holds more than eight entries, specifically to cap the worst case).<\/p>\n
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What is the difference between a stack and a queue, and when would you use each?<\/span>Stack vs Queue<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
A stack is last-in-first-out: whatever you pushed most recently is the first thing that comes back out with pop. A queue is first-in-first-out: the oldest item still enqueued is the first one dequeued. Both support O(1) insert and remove when implemented with an array or linked list, the difference is purely which end you’re allowed to touch.<\/p>\n
Stacks show up anywhere you need to unwind something in reverse order of how it happened: undo\/redo in an editor, matching parentheses or tags, and the call stack a program uses to track function returns. Depth-first search on a graph or tree is naturally a stack, whether it’s the recursion stack or one you manage explicitly. Queues fit anywhere order of arrival matters: task schedulers, message buffers between producers and consumers, and breadth-first search, where you want to fully explore one level before moving to the next.<\/p>\n
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What is Palantir's ontology, and why is it described as the foundation of Foundry?<\/span>Palantir Ontology<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
The ontology is the layer that turns a customer’s raw, disconnected data, rows in a warehouse table, files in a data lake, readings from a sensor feed, into real-world objects with names, properties, and relationships. Instead of every dashboard or pipeline defining its own private notion of what an “Aircraft” or a “Shipment” is, the ontology defines it once: an Aircraft object type with properties like tail number and maintenance status, linked to Flight objects and Part objects, plus actions that are allowed on it, like scheduling a maintenance check.<\/p>\n
That’s why it’s described as foundational rather than just another feature: every application built in Foundry, whether it’s a Workshop dashboard, a pipeline transform, or an AIP agent, reads from and writes back to the same ontology. Update an object’s status in one application and it’s immediately visible everywhere else that references it, because there’s one shared model instead of many copies of the data drifting out of sync across many different tools.<\/p>\n
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What is the difference between an inner join and a left join in SQL?<\/span>SQL Joins<\/span>Easy<\/span><\/span><\/span><\/button><\/p>\n
An inner join only returns rows where the join condition matches in both tables, anything without a match on either side gets dropped from the result entirely. A left join returns every row from the left table no matter what, and if there’s no matching row on the right, it fills those columns in with NULL instead of dropping the row.<\/p>\n
The common gotcha is filtering on the right table’s columns in the WHERE clause after a left join. If you write a WHERE condition on orders.status after a LEFT JOIN, any customer with no orders has NULL for that column, and NULL never equals a literal value, so that customer silently disappears from the results, turning your left join back into an effective inner join. The fix is to move that condition into the ON clause instead, so it’s applied while the join is happening rather than filtering the joined result afterward.<\/p>\n
sql<\/span>Copy<\/button><\/div>\n\n\u2014 wrong: silently drops customers with no orders\n\nSELECT c.name, o.status\n\nFROM customers c\n\nLEFT JOIN orders o ON o.customer_id = c.id\n\nWHERE o.status = \u2018shipped\u2019;\n\u2014 right: keeps every customer, matches shipped orders where they exist\n\nSELECT c.name, o.status\n\nFROM customers c\n\nLEFT JOIN orders o ON o.customer_id = c.id AND o.status = \u2018shipped\u2019;\n<\/code><\/pre><\/div><\/span>Medium questions<\/h2>22<\/span><\/div><\/div><\/div>\n
Find the longest substring with at most K distinct characters<\/span>Sliding Window<\/span>Medium<\/span><\/span><\/span><\/button><\/p>\n
Sliding window with a hash map tracking character frequencies. Expand the right pointer. When the map has more than K distinct keys, shrink from the left until it’s back to K. Track the maximum window size seen.<\/p>\n
Reported from multiple Palantir phone screen and OA write-ups between 2024 and 2025. The follow-up almost always asks what changes if duplicate characters within the window matter, or if the input is a stream rather than a static string. Prepare both variants.<\/p>\n
python<\/span>Copy<\/button><\/div>\n\ndef longest_k_distinct(s: str, k: int) -> int:\n\n freq = {}\n\n left = 0\n\n best = 0\n for right, ch in enumerate(s):\n\n freq[ch] = freq.get(ch, 0) + 1\n\n while len(freq) > k:\n\n left_ch = s[left]\n\n freq[left_ch] -= 1\n\n if freq[left_ch] == 0:\n\n del freq[left_ch]\n\n left += 1\n\n best = max(best, right \u2013 left + 1)\n return best\n<\/code><\/pre><\/div>Merge overlapping intervals<\/span>Intervals<\/span>Medium<\/span><\/span><\/span><\/button><\/p>\n
Sort by start time. Walk through the list. If the current interval’s start is within the last merged interval’s end, extend the end. If not, push a new interval. Return the merged list.<\/p>\n
Palantir frames this as scheduling: “given a list of hospital staff shifts, return the continuous coverage windows.” The algorithm is the same, but the framing requires a quick mental translation. Reported from a 2024 online assessment.<\/p>\n
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Minimum number of conference rooms required for a list of meeting time intervals<\/span>Intervals<\/span>Medium<\/span><\/span><\/span><\/button><\/p>\n
Sort meetings by start time. Use a min-heap keyed on end time. For each meeting, if the earliest-ending room in the heap frees up before the new meeting starts, pop it and reuse that room; otherwise push a new room onto the heap. The heap’s size at any point is the number of rooms in use, and its maximum size across the whole pass is the answer.<\/p>\n
Palantir’s real-world framing on this one is usually a booking system for shared lab equipment across research teams rather than office meetings, which is the same overlap-counting problem wearing different clothes. Reported from a 2025 OA and a phone screen write-up. The follow-up asks you to also return which room number each meeting was assigned, which forces you to track room identity in the heap rather than just a count.<\/p>\n
python<\/span>Copy<\/button><\/div>\n\nimport heapq\ndef min_meeting_rooms(intervals: list[list[int]]) -> int:\n\n if not intervals:\n\n return 0\n\n intervals.sort(key=lambda x: x[0])\n\n rooms = [] # min-heap of end times\n for start, end in intervals:\n\n if rooms and rooms[0] <= start:\n\n heapq.heappop(rooms)\n\n heapq.heappush(rooms, end)\n return len(rooms)\n<\/code><\/pre><\/div>Subdomain visit count<\/span>HashMap<\/span>Medium<\/span><\/span><\/span><\/button><\/p>\n
Parse each entry into its domain hierarchy. For “discuss.leetcode.com”, you increment counts for “discuss.leetcode.com”, “leetcode.com”, and “com” separately. Use a hash map of domain strings to visit counts. Then format the output.<\/p>\n
Reported from Palantir OAs across multiple candidate accounts. The wrinkle is parsing correctly: split on “.” and build each suffix from left to right. The solution looks simple but candidates who try to hardcode the dot-splitting logic get tripped up on edge cases like single-label domains.<\/p>\n
python<\/span>Copy<\/button><\/div>\n\nfrom collections import defaultdict\ndef subdomain_visits(cpdomains: list[str]) -> list[str]:\n\n counts = defaultdict(int)\n\n for entry in cpdomains:\n\n count, domain = entry.split()\n\n count = int(count)\n\n parts = domain.split(\u201c.\u201d)\n\n for i in range(len(parts)):\n\n counts[\u201d.\u201d.join(parts[i:])] += count\n\n return [f\u201d{v} {k}\u201d for k, v in counts.items()]\n<\/code><\/pre><\/div>All ancestors of a node in a directed acyclic graph<\/span>Graphs<\/span>Medium<\/span><\/span><\/span><\/button><\/p>\n
Build a reverse adjacency list (child maps to parents). Run DFS or BFS backward from each target node to collect all reachable ancestors. Sort and return. The key insight is that the forward graph gives you descendants; reversing the edges gives you ancestors.<\/p>\n
Reported directly from Palantir phone screen write-ups in 2024 and 2025. A common follow-up: “what if the graph has cycles?” The answer changes the algorithm to cycle-detection-aware traversal using a visited set with a recursion stack. Palantir likes this kind of constraint mutation mid-problem.<\/p>\n
python<\/span>Copy<\/button><\/div>\n\nfrom collections import defaultdict\ndef get_ancestors(n: int, edges: list[list[int]]) -> list[list[int]]:\n\n parents = defaultdict(list)\n\n for u, v in edges:\n\n parents[v].append(u)\n result = []\n\n for node in range(n):\n\n ancestors = set()\n\n stack = [node]\n\n while stack:\n\n curr = stack.pop()\n\n for p in parents[curr]:\n\n if p not in ancestors:\n\n ancestors.add(p)\n\n stack.append(p)\n\n result.append(sorted(ancestors))\n\n return result\n<\/code><\/pre><\/div>Find players with zero or one losses in a tournament bracket<\/span>HashMap<\/span>Medium<\/span><\/span><\/span><\/button>