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Fannie Mae interview questions

3 practice questions in the style of Fannie Mae's interviews, covering Data Manipulation (SQL/Python), Behavioral & Leadership.

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Fannie Mae interview questions by the numbers

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Katkat has 3 Fannie Mae interview questions. The largest topics are Data Manipulation (SQL/Python) (67%) and Behavioral & Leadership (33%). By difficulty: 100% medium. The most common round is Onsite (67%).

Topics

Difficulty

  • Medium100% · 3

Interview rounds

  • Onsite67% · 2
  • Technical Screen33% · 1

Roles

  • Data Scientist100% · 3

Showing 1–3 of 3 results

  • Assess SQL joins, unions, windows, dedup, and pandasMedium

    This question evaluates proficiency in SQL and pandas data-manipulation techniques, including aggregations with NULL semantics, various join types, set operations and deduplication, window functions, and reconciling normalized and denormalized sources.

    Fannie MaeData ScientistData Manipulation (SQL/Python)Technical Screen
  • Design Test Cases for UI Change: Ensuring Quality and FunctionalityMedium

    Evaluates mission-aware QA collaboration for UI and backend changes in a data-science role. Strong answers compare test layers, define acceptance criteria, cover accessibility and auditability, and design robust test cases.

    Fannie MaeData ScientistBehavioral & LeadershipOnsite
  • Understand SQL Aggregations and Joins: Key Differences ExplainedMedium

    This question evaluates a candidate's competency in SQL aggregations (COUNT, SUM, AVG, MIN, MAX), join semantics (INNER, LEFT, RIGHT, FULL OUTER), set operations (UNION vs UNION ALL), window functions, view versus table trade-offs, duplicate detection and removal, query optimization techniques, and pandas DataFrame operations (merge, join, concat) within the Data Manipulation (SQL/Python) domain. It is commonly asked because it assesses both conceptual understanding (function purposes and join semantics) and practical application (writing efficient queries, deduplicating data, and translating patterns between SQL and pandas), thereby probing correctness and performance reasoning.

    Fannie MaeData ScientistData Manipulation (SQL/Python)Onsite

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