Sharechat Question Paper: 2026 Practical Guide & Examples

When candidates and students search for a sharechat question paper, they encounter a wide spectrum of material. On the social platform itself, millions of users share regional study materials, board exam revision sets, and daily quiz cards across regional languages. Concurrently, technical job seekers targeting Mohalla Tech—the parent company behind ShareChat and Moj—search for actual technical screening papers to understand how the company evaluates data literacy, high-throughput event processing, and feed engagement metrics.

This practical guide breaks down both facets: clarifying how user-generated academic question sheets circulate on the platform, and providing an exhaustive technical blueprint for passing ShareChat data analytics screening evaluations. You will explore structured skill matrices across experience tiers, an interactive SQL code fixture for feed retention analysis, and product analytics scenarios modeled after consumer social scale.

Understanding the Two Search Intents: Academic Sharing vs. Technical Screening

To navigate resources effectively, readers must distinguish between community-curated educational material on the app and company-administered hiring evaluations.

1. The Educational and Quiz Ecosystem on ShareChat

ShareChat is one of India's leading vernacular social platforms, supporting over 15 regional languages including Hindi, Tamil, Telugu, Kannada, Malayalam, and Marathi. Within the app, educational creators and student networks frequently post revision materials under trending tags.

• Board Exam Model Papers: Queries such as sharechat 10th question paper social science or state matriculation guides emerge because regional study groups circulate scanned PDF pages, previous-year question sets, and handwritten solution keys before annual examinations.
• ShareChat Quiz and Trivia: Interactive polls, daily current affairs quizzes, and competitive exam preparation questions (UPSC, SSC, Banking) represent another high-volume format shared via short images and text carousels.
• Verification and Academic Integrity: While community-shared papers offer rapid peer-to-peer revision, they are user-generated uploads rather than officially accredited examination boards. Students should cross-check any circulated paper against state board portals (such as CBSE, KSEEB, or State Education Boards) to avoid outdated syllabus revisions.

2. The ShareChat (Mohalla Tech) Technical Assessment Paper

For data analysts, analytics engineers, and business intelligence specialists, the term refers to the pre-interview technical test administered via platforms like HackerRank or Codility.

According to hiring insights documented across the ShareChat Engineering Blog, technical evaluations emphasize scalable query design, deterministic handling of edge cases, and high-throughput data processing. Because ShareChat operates a massive recommendation engine serving hundreds of millions of video views daily, their screening question sets do not test abstract textbook definitions. Instead, they present real-world data schemas simulating user feeds, video impressions, creator tips, and push notification clicks.

ShareChat Technical Screening Architecture

The initial quantitative filter for data analytics roles at ShareChat evaluates candidate competence in querying high-velocity, semi-structured event tables.

The test environment enforces strict deterministic grading. Your queries run against hidden test cases that evaluate edge conditions: null session timestamps, zero-second video plays, timezone shifts across Indian Standard Time (IST), and skewed creator follower distributions.

Experience Tiers & Technical Skill Matrix

The technical depth demanded on a ShareChat screening paper varies significantly across seniority levels. Below is the structured skill matrix evaluating what candidates must demonstrate at each stage.

Candidates aiming for mid and senior roles are expected to write modular, maintainable SQL utilizing Common Table Expressions (CTEs) rather than fragile multi-level subqueries. To build proficiency with multi-table queries, explore our deep dive on SQL Joins.

Interactive Code Sandbox Fixture: Short-Form Video Retention

To provide verifiable utility, let us analyze a canonical problem featured on consumer social technical assessments: The Feed Session Completion and Repeat Viewer Metric.

Scenario & Business Context

In a short-form video feed (similar to Moj or ShareChat), product managers need to measure video engagement quality. In this simulated interview problem, an engagement event is classified as a completed play when the user watches the entire video duration (watch_time_seconds >= video_duration_seconds). Furthermore, the analytics team wants to determine whether a user returns to watch another video within the same calendar day.

Input Data Fixture: user_video_sessions

Below is a structured input table representing simulated user video events over a sample observation window:

Problem Statement

Write an ANSI-SQL query that returns:

1. Each user_id and the date of activity (event_date).
2. Total videos watched on that date (total_plays).
3. Total completed videos on that date (completed_plays), defined where watch_time_seconds >= video_duration_seconds.
4. The time gap in minutes (gap_to_next_play_minutes) between the user's first and second video play on that day, using window functions.

Reproducible SQL Implementation

WITH daily_user_events AS (
    SELECT
        user_id,
        DATE(event_time) AS event_date,
        event_time,
        watch_time_seconds,
        video_duration_seconds,
        -- Check completion condition (user watched full video duration)
        CASE 
            WHEN watch_time_seconds >= video_duration_seconds THEN 1 
            ELSE 0 
        END AS is_completed,
        -- Identify sequence order of video consumption per user per day
        ROW_NUMBER() OVER (
            PARTITION BY user_id, DATE(event_time) 
            ORDER BY event_time ASC
        ) AS play_order,
        -- Peek forward to the subsequent video event timestamp
        LEAD(event_time) OVER (
            PARTITION BY user_id, DATE(event_time) 
            ORDER BY event_time ASC
        ) AS next_event_time
    FROM user_video_sessions
    WHERE video_duration_seconds > 0
)
SELECT
    user_id,
    event_date,
    COUNT(*) AS total_plays,
    SUM(is_completed) AS completed_plays,
    -- Calculate interval between 1st and 2nd play in minutes
    ROUND(
        COALESCE(
            MAX(
                CASE 
                    WHEN play_order = 1 AND next_event_time IS NOT NULL 
                    THEN EXTRACT(EPOCH FROM (next_event_time - event_time)) / 60.0 
                    ELSE NULL 
                END
            ), 
            0
        ), 
        2
    ) AS gap_to_next_play_minutes
FROM daily_user_events
GROUP BY user_id, event_date
ORDER BY event_date ASC, user_id ASC;

Deterministic Output Fixture

Executing this query against the synthetic test table yields the following deterministic result set:

Critical Edge Cases Evaluated in Screening Tests

1. Zero-Duration Videos: If an upstream client event logs video_duration_seconds = 0, calculating completion comparisons on invalid durations causes logical errors. The CTE explicitly adds WHERE video_duration_seconds > 0.
2. Single-Play Sessions: Users with only one play on a given date (u3 on Nov 1, or u1 on Nov 2) have no subsequent event (LEAD returns NULL). The calculation uses COALESCE(..., 0) to guarantee safe numerical output.
3. Multi-Day Activity: User u1 engaged across two separate dates. Partitioning the window function by both user_id and DATE(event_time) prevents timestamps from bleeding across consecutive days.

Mastering window functions like ROW_NUMBER, LEAD, and LAG is essential for cracking these patterns. For the formal SQL syntax specifications and partition execution semantics, see the official PostgreSQL Window Functions Documentation, and practice our dedicated modules on SQL Window Functions.

ShareChat Product Metrics & Scenario Questions

Beyond raw syntax, the ShareChat interview loop tests how effectively you translate product challenges into analytical frameworks. Here are three representative questions frequently encountered on screening tests and technical rounds:

1. Measuring Feed Stickiness: The DAU / MAU Ratio

• The Question: ShareChat monitors regional language communities across Tier-2 and Tier-3 Indian cities. How would you determine whether a drop in Daily Active Users (DAU) in a specific dialect is caused by content exhaustion or technical latency?
• Analytical Approach:
  1. Segment the user base by app version and device tier (e.g., budget Android devices with limited RAM).
  2. Compute the L14 user stickiness index: count how many days out of the last 14 days active users opened the app.
  3. Compare dwell time per session against video start failure rates. If video start failures spiked, the issue is infrastructure-driven. If dwell time decreased while playback remained healthy, content freshness or creator churn is the root cause.

2. Dwell Time vs. Click-Through Rate (CTR) Trade-Off

• The Question: An algorithmic update to the explore feed causes post click-through rates to rise substantially, but overall average user session duration declines sharply. Would you recommend rolling out this recommendation model?
• Analytical Approach: A higher CTR paired with reduced session duration indicates clickbait inflation. Users click promising thumbnails, quickly realize the content does not match expectations, and exit the feed. The candidate should propose a composite metric—such as Satisfied Watch Hours (sessions with completed video plays)—as the primary decision gate rather than raw CTR.

3. Push Notification Re-Engagement Attribution

• The Question: ShareChat sends millions of personalized push notifications daily in vernacular scripts. How do you construct an SQL query to attribute session opens to notifications without double-counting organic app launches?
• Analytical Approach: Join the notification_sent log with app_open_events using an explicit attribution window where the application launch occurs immediately following dispatch. For example, filtering where the session launch occurs after dispatch within an allowed lookback threshold (app_open_time >= sent_time AND app_open_time <= sent_time + lookback_interval). Rank notification events using ROW_NUMBER() OVER (PARTITION BY user_id, session_id ORDER BY sent_time DESC) to implement a clean last-touch attribution model.

4-Week Technical Preparation Blueprint

To reliably pass the ShareChat data analytics screening question paper, follow this structured 4-week sprint:

Week 1: Advanced SQL Aggregation & Multi-Table Joins
Week 2: SQL Window Functions (Sessionization & Running Totals)
Week 3: Cohort Retention & Funnel Drop-Off Analysis
Week 4: Product Analytics Case Studies & Mock Screenings

• Week 1 (Foundations): Master complex joins, subqueries, grouping sets, and handling NULL values with COALESCE. Practice writing clean, idiomatic SQL without redundant operations.
• Week 2 (Window Functions): Solve real-world window function problems covering ROW_NUMBER(), DENSE_RANK(), NTILE(), and comparative offset functions (LAG() and LEAD()).
• Week 3 (Cohort Analytics): Build SQL scripts that calculate Day-1, Day-7, and Day-30 user retention matrices from raw event logs. Work through multi-stage conversion funnels.
• Week 4 (Execution & Timed Mocks): Simulate 90-minute timed environments. Focus on code readability, proper alias naming, and immediate edge case validation before submitting code.

Whether you are deciphering student study papers shared across regional channels or aiming to join Mohalla Tech as a high-impact data analyst, structured technical preparation and rigorous problem practice make all the difference.