Meta interview questions

Showing 121–150 of 666 results

• Compute sample size and test duration correctlyHard

  This question evaluates competency in experiment design and statistical power calculations, including sample size estimation, variance misspecification effects, multiple-comparison corrections, sequential monitoring boundaries, and adjustments for covariates or clustering.

  MetaData ScientistStatistics & MathTechnical Screen
• Compute shop visibility and intent metrics in SQLMedium

  This question evaluates SQL-based data manipulation and analytical competencies in the Data Manipulation (SQL/Python) domain, focusing on deduplication, time-zone-aware timestamp handling, window functions, aggregation, and construction of visibility, CTR, and intent metrics for shops.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute sliding-window mediansMedium

  This question evaluates proficiency in designing and analyzing efficient sliding-window algorithms and data structures for computing running medians, including correct handling of duplicates, deletions, and the convention for defining the median when k is even.

  MetaMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Compute sparse dot product and count islandsMedium

  This question evaluates competency in designing efficient algorithms for sparse data representations and for identifying connected components in grids, testing skills in handling large inputs, memory-efficient representations, and traversal/graph concepts.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute the Dot Product of Sparse VectorsHard

  Compute the dot product of two sparse vectors stored as sorted index-value pairs without expanding them. A two-pointer traversal should visit only nonzero entries, multiply matching indices, and accumulate safely in a wide integer type.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Compute the next lexicographic permutationMedium

  This problem tests your ability to manipulate array elements in-place to find the next lexicographically ordered sequence, focusing on understanding the properties of permutations.

  MetaMachine Learning EngineerCoding & AlgorithmsOnsite
• Compute this-year spend share of last-year whalesEasy

  This question tests the ability to perform cohort analysis and time-based filtering to calculate the contribution of high-value segments across different fiscal years.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Compute time-spent percentage by app categoryMedium

  This question evaluates proficiency in data manipulation and analytics, specifically SQL/Python skills for joining tables, aggregating session durations, handling missing app-category mappings, and computing percentage time spent over a recent seven-day window.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Compute unconnected 60s posts and reactions averagesMedium

  This question evaluates data manipulation skills in SQL and Python, focusing on deduplication, join logic between event and reaction tables, date handling for string-typed dates, and aggregation to compute per-post metrics.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute unmatched parentheses lengthMedium

  This problem tests your ability to use stack-based logic to track balanced sequences and identify the length of characters that remain unmatched after processing all valid pairs.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Compute unread and multi-account user percentagesMedium

  This question evaluates understanding of SQL data manipulation skills such as aggregations, joins, NULL handling, deduplication reasoning, and user- versus person-level computations for accurate percentage metrics.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute vertical order of a BSTMedium

  This question evaluates understanding of binary tree traversals and coordinate-based ordering, including tie-breaking by breadth-first visitation order, plus competency in algorithmic time and space complexity analysis and strategies for handling deep recursion and streaming large inputs.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute view prevalence from views and labelsMedium

  This question evaluates a candidate's competency in temporal joins, stateful deduplication of moderation decisions, time-aware aggregation, and prevalence metric calculations within content-moderation analytics.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute weighted sum of nested listsMedium

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compute weighted sum of nested lists states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Construct a 95% Confidence Interval for Comment CountsMedium

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Construct a 95% Confidence Interval for Comment Counts states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistStatistics & MathOnsite
• Convert BST to sorted doubly listMedium

  This question evaluates understanding of binary search tree properties, in-order traversal, in-place pointer manipulation, and algorithmic complexity when converting tree nodes into a sorted doubly linked list.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Core Behavioral ReflectionsMedium

  Practice core Product Manager behavioral reflections covering professional failure, proposing an opposed idea, biggest success, and data ethics. The solution emphasizes STAR-L structure, metrics, ownership, influence, privacy, fairness, and learning.

  MetaProduct ManagerBehavioral & LeadershipOnsite
• Count Distinct Values in a Sorted Array When K Is SmallMedium

  Count distinct values in a sorted array efficiently when the number of unique values is much smaller than the array length. The challenge tests adapting complexity to output diversity rather than raw length, meeting an O(k log n) target with constant auxiliary space, and handling empty or uniform arrays.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Count Subarrays with a Target SumMedium

  Tests the ability to use prefix sums and hash tables to count contiguous subarrays summing to a target in linear time, handling negative values and zeros.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Count heavy callers in 7 daysMedium

  This question tests the ability to handle temporal windowing and aggregation to identify user behavior patterns based on call activity frequency.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Count palindrome substrings in a stringMedium

  This question evaluates proficiency in string algorithms, algorithmic design, and complexity analysis by targeting palindromic substring counting, string manipulation, and conceptual linear-time techniques.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Count subarrays equal to targetMedium

  This problem tests your ability to use a hash map to track prefix sums, allowing for an efficient solution to a subarray counting problem.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Count subarrays summing to targetMedium

  This question evaluates understanding of prefix-sum concepts, use of linear-time data structures, and competence in handling negative numbers and edge cases such as zero targets and empty prefixes.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Count three-digit numbers with distinct digitsHard

  This problem tests your ability to count numbers satisfying digit-based constraints within a specific range using combinatorial logic or efficient iteration.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Debug and optimize React performance issuesMedium

  This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Debug and optimize React performance issues states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaSoftware EngineerSystem DesignTechnical Screen
• Debug and optimize a card-drawing strategyMedium

  This problem tests your ability to simulate card-drawing probabilities, debug flawed logic in iterative selection strategies, and optimize performance for repeated simulations.

  MetaMachine Learning EngineerCoding & AlgorithmsOnsite
• Decide and experiment on Group Call featureMedium

  This question evaluates a data scientist's proficiency in product analytics, causal inference, metric engineering, experimentation design, and post‑launch diagnostics within the Analytics & Experimentation domain.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Decide event notification launch via experimentsMedium

  This question tests the ability to design a robust A/B test in the presence of network effects, interference, and cannibalization within a social notification ecosystem.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Decide palindrome within k deletionsMedium

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Decide palindrome within k deletions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Decide when CTR falls but revenue risesHard

  This question evaluates understanding of A/B experimentation, auction-based ad metrics, metric decomposition for revenue impact, causal diagnostics for hidden heterogeneity (e.g., Simpson’s paradox), and executive-level results presentation within the Analytics & Experimentation domain.

  MetaData ScientistAnalytics & ExperimentationOnsite