Question 1

How would you handle memory constraints when processing a large dataset in Python?

Accepted Answer

**Strategies:** (1) Chunked read. (2) Dtypes—category, int8/16/32. (3) Generators. (4) Dask/Vaex—out-of-core. (5) Disk/SQL. (6) Streaming. **Why:** Fit in memory. **Production:** memory_profiler. **Scalability:** Chunk + aggregate.

Question 2

How would you process a 10TB dataset on a single machine in Python?

Accepted Answer

**Reality:** Not practical to load. **Options:** (1) Stream/chunk; aggregate. (2) SQL—bulk load; query. (3) Dask/Vaex. (4) Recommend distributed (Spark). **If must:** Partition file; process parts; merge. **Why:** Memory limit....

Question 3

Implement a recursive algorithm to find the nth Fibonacci number.

Accepted Answer

**Why Recursion Fails at Scale:** The naive recursive fib(n) = fib(n-1) + fib(n-2) exhibits O(2^n) time and O(n) stack depth. Each subproblem is recomputed exponentially—fib(40) triggers ~1B calls. In distributed data pipelines, this pattern causes task explosion.

**Architectural Trade-offs:** (1) Memoization (@lru_cache): O(n) time, O(n) space—suitable for bounded n in single-process. (2) Iterative DP: O(n) time, O(1) space—production default....

Question 4

Write a Python script to parse a large JSON file, filter records based on a condition, and write the result to a database.

Accepted Answer

**Why Streaming:** Loading 10GB JSON kills memory. Use JSON Lines (ndjson)—one JSON object per line; iterate line-by-line. For nested: ijson for streaming parse.

**Pattern:** Open file, for line in f: obj=json.loads(line); if cond: batch.append(obj). Batch insert (executemany) every 1000 rows. Commit in batches.

**Production:** Use connection pooling. Progress logging. Handle malformed lines (try/except, dead-letter)....

Question 5

Write code to merge two sorted arrays without using extra space.

Accepted Answer

**LeetCode 88:** nums1 has size m+n, m elements; nums2 has n. Merge into nums1. Fill from end (k=m+n-1) to avoid overwriting. Compare nums1[i] vs nums2[j], place larger at k.

def merge(nums1, m, nums2, n):
    i, j, k = m-1, n-1, m+n-1
    while j >= 0:
        if i >= 0 and nums1[i] > nums2[j]:
            nums1[k] = nums1[i]; i -= 1
        else:
            nums1[k] = nums2[j]; j -= 1
        k -= 1

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