Media.net Placement Papers 2026 | AdTech Interview Questions & Complete Guide

Meta Description: Crack Media.net campus placements 2026 with real placement papers, aptitude questions, coding problems, and HR interview tips. Fresher CTC: ₹12–22 LPA. Complete guide for software engineering and product roles at one of India's top AdTech companies.

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About Media.net

Media.net is one of the world's largest advertising technology (AdTech) companies, operating the second-largest contextual advertising marketplace globally. Founded in 2010 and headquartered in Dubai, UAE, with major operations in Mumbai, Media.net powers advertising for hundreds of thousands of publishers and connects them with leading advertisers including Microsoft, Yahoo, and other Fortune 500 companies. The company manages ad monetization for premium publishers and provides search advertising solutions used by millions of websites worldwide.

Media.net's technology platform processes billions of ad requests daily, leveraging machine learning, real-time bidding (RTB), and advanced contextual targeting algorithms. The Mumbai engineering office is the company's primary technology hub, housing teams working on ad serving infrastructure, ML-based bidding systems, publisher analytics, and advertiser optimization tools. Despite being less well-known than the big tech companies, Media.net is considered a hidden gem in India's startup-to-scale-up ecosystem — known for challenging engineering problems, excellent work culture, and strong compensation.

The company actively recruits from top engineering colleges including IITs, NITs, BITS Pilani, and select private universities (VJTI, DAIICT, Thapar, etc.). For freshers, Media.net offers roles in Software Engineering (Backend, Full Stack), Data Engineering, Machine Learning Engineering, and Product Management. The compensation ranges from ₹12 LPA to ₹22 LPA for freshers, supplemented by performance bonuses and employee stock options (ESOPs) that have significant upside potential. The company is known for a strong engineering-first culture and relatively flat hierarchy.

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Eligibility Criteria

Parameter	Requirement
Degree	B.Tech / B.E. / M.Tech / MCA
Branches	CS, IT, ECE (core tech roles); any engineering (product/analytics)
Minimum CGPA	7.0 / 10
Active Backlogs	None allowed at time of joining
Historical Backlogs	Maximum 1 acceptable (case-by-case)
Year of Graduation	2026 (current batch)
10th Marks	≥ 65%
12th Marks	≥ 65%
Bond	No bond; service agreement not required

Tip: Media.net values strong project experience and internships alongside academics. A slightly lower CGPA with strong practical experience can still lead to shortlisting.

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Selection Process

Media.net's selection process is thorough but relatively candidate-friendly compared to finance firms:

1. Online Application / CV Shortlisting — Applications submitted through campus portal or Media.net's careers page. Shortlisting considers CGPA, relevant projects, internships, and GitHub activity.

2. Online Coding Round (HackerEarth/HackerRank) — 3–4 coding problems ranging from Easy to Medium difficulty, timed at 90 minutes. Tests core DSA and problem-solving ability.

3. Technical Interview Round 1 (DSA Focus) — 45–60 minutes. Covers data structures, algorithms, and problem-solving. Interviewers assess not just correctness but code quality and the ability to optimize solutions iteratively.

4. Technical Interview Round 2 (CS Fundamentals + System Design) — Covers DBMS (SQL, normalization), Operating Systems, Networking, OOPS, and a beginner-level system design question. For ML roles, this includes ML fundamentals and statistics.

5. Manager / Culture Fit Interview — 30 minutes with an engineering manager. Assesses team collaboration, learning mindset, communication, and interest in AdTech as a domain.

6. HR Interview — Brief round for standard HR questions. Offers are typically extended within 3–5 business days.

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Exam Pattern

Section	Topics	Questions	Time
Quantitative Aptitude	Percentages, Averages, Ratio, Time-Work	15	20 min
Logical Reasoning	Patterns, Puzzles, Data Interpretation	10	15 min
Verbal Ability	Reading Comprehension, Grammar	10	15 min
Coding	DSA (Easy to Medium LeetCode level)	3–4	45–60 min
Total		~40	~110 min

The aptitude section is straightforward compared to finance firms. The coding section is the primary differentiator.

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Practice Questions with Detailed Solutions

Aptitude Questions

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Q1. A shopkeeper marks his goods 40% above cost price and then gives a 25% discount. What is his profit or loss percentage?

Solution:

Let CP = ₹100 Marked Price = 100 + 40 = ₹140 Selling Price = 140 × (1 – 0.25) = 140 × 0.75 = ₹105

Profit = 105 – 100 = ₹5 Profit % = 5%

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Q2. A car travels 120 km in 2 hours and then 90 km in 1.5 hours. What is the average speed for the total journey?

Solution:

Total distance = 120 + 90 = 210 km Total time = 2 + 1.5 = 3.5 hours

Average speed = 210 / 3.5 = 60 km/h

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Q3. Find the number of ways to select 3 boys and 2 girls from a group of 6 boys and 4 girls.

Solution:

Ways = C(6,3) × C(4,2) = 20 × 6 = 120 ways

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Q4. Simple Interest on ₹5,000 for 3 years at 8% p.a. and Compound Interest on the same principal for 2 years at 10% — which gives more interest?

Solution:

SI = (5000 × 8 × 3) / 100 = ₹1,200

CI: A = 5000 × (1.1)² = 5000 × 1.21 = ₹6,050 CI = 6050 – 5000 = ₹1,050

SI gives more interest in this case: ₹1,200 > ₹1,050

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Q5. A train 150m long passes a pole in 15 seconds. How long will it take to pass a platform 300m long?

Solution:

Speed = 150/15 = 10 m/s Distance to cross platform = 150 + 300 = 450m Time = 450/10 = 45 seconds

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Q6. In a class of 40 students, the average mark is 70. If the marks of two students (85 and 92) were wrongly recorded as 58 and 69, what is the corrected average?

Solution:

Original total = 40 × 70 = 2800 Wrong entries sum = 58 + 69 = 127 Correct entries sum = 85 + 92 = 177 Correction = 177 – 127 = +50

Corrected total = 2800 + 50 = 2850 Corrected average = 2850/40 = 71.25

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Q7. A series: 3, 7, 13, 21, 31, ___. Find the next term.

Solution:

Differences: 4, 6, 8, 10 → next difference = 12 31 + 12 = 43

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Q8. If x% of y is 12 and y% of x is also 12, find x × y.

Solution:

x% of y = xy/100 = 12 So xy = 1200 y% of x = xy/100 = 1200/100 = 12 ✓

x × y = 1200

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Coding Questions

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Q9. Design an LRU (Least Recently Used) Cache.

from collections import OrderedDict

class LRUCache:
    def __init__(self, capacity):
        self.capacity = capacity
        self.cache = OrderedDict()
    
    def get(self, key):
        if key not in self.cache:
            return -1
        self.cache.move_to_end(key)  # Mark as recently used
        return self.cache[key]
    
    def put(self, key, value):
        if key in self.cache:
            self.cache.move_to_end(key)
        self.cache[key] = value
        if len(self.cache) > self.capacity:
            self.cache.popitem(last=False)  # Remove LRU item

# Both get and put: O(1)
# Media.net uses caching extensively in ad serving systems

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Q10. Write a function to detect a cycle in a linked list.

def hasCycle(head):
    slow = fast = head
    while fast and fast.next:
        slow = slow.next
        fast = fast.next.next
        if slow == fast:
            return True
    return False

# Floyd's Cycle Detection Algorithm
# Time: O(n) | Space: O(1)

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Q11. Given a list of ad impressions (click=1, no-click=0), find the maximum consecutive clicks.

def maxConsecutiveOnes(nums):
    max_count = count = 0
    for n in nums:
        if n == 1:
            count += 1
            max_count = max(max_count, count)
        else:
            count = 0
    return max_count

# Highly relevant to click-through rate analysis in AdTech!
# Example: [1,1,0,1,1,1] → 3
# Time: O(n) | Space: O(1)

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Q12. Given an array of ad revenue per day, find the maximum revenue from a contiguous campaign window of size k.

def maxSlidingWindow(nums, k):
    from collections import deque
    dq = deque()
    result = []
    
    for i, num in enumerate(nums):
        # Remove elements outside window
        while dq and dq[0] < i - k + 1:
            dq.popleft()
        # Remove smaller elements (they can't be max)
        while dq and nums[dq[-1]] < num:
            dq.pop()
        dq.append(i)
        if i >= k - 1:
            result.append(nums[dq[0]])
    return result

# Example: nums=[3,1,2,5,8,4,2,7], k=3 → [3,5,8,8,8,7]
# Time: O(n) | Space: O(k)

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Q13. Serialize and Deserialize a Binary Tree (useful for storing ad targeting trees).

class Codec:
    def serialize(self, root):
        if not root:
            return 'null,'
        return str(root.val) + ',' + self.serialize(root.left) + self.serialize(root.right)
    
    def deserialize(self, data):
        values = iter(data.split(','))
        
        def helper():
            val = next(values)
            if val == 'null':
                return None
            node = TreeNode(int(val))
            node.left = helper()
            node.right = helper()
            return node
        
        return helper()

# Time: O(n) | Space: O(n) for both operations

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Q14. Top K Frequently Occurring Keywords in an Ad Campaign.

import heapq
from collections import Counter

def topKFrequent(words, k):
    count = Counter(words)
    # Min-heap of size k: (-freq, word) for stable sort
    heap = []
    for word, freq in count.items():
        heapq.heappush(heap, (-freq, word))
    return [heapq.heappop(heap)[1] for _ in range(k)]

# Example: words=["click","view","click","buy","view","click"], k=2
# → ["click", "view"]
# Time: O(n + k log n) | Space: O(n)

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Q15. SQL Question — Media.net Database (AdTech-style)

Problem: You have an ad_impressions table with columns: impression_id, publisher_id, advertiser_id, revenue, date. Write a query to find the top 3 publishers by total revenue for the month of February 2026.

Solution:

SELECT 
    publisher_id,
    SUM(revenue) AS total_revenue
FROM ad_impressions
WHERE 
    date >= '2026-02-01' 
    AND date < '2026-03-01'
GROUP BY publisher_id
ORDER BY total_revenue DESC
LIMIT 3;

Explanation:

• WHERE filters for February 2026 (using range for index efficiency vs. MONTH() function)
• GROUP BY aggregates revenue per publisher
• ORDER BY DESC + LIMIT 3 returns top 3
• Index on (date, publisher_id, revenue) would optimize this query significantly

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HR Interview Questions & Sample Answers

Q1. Why Media.net and not a more well-known tech giant?

Sample Answer: "Media.net sits in the sweet spot for me — it's large enough to have technically challenging, high-scale problems (billions of ad requests daily), but still engineering-driven enough that a fresher can have real ownership and impact quickly. I'm genuinely interested in AdTech as a domain — the intersection of ML, real-time systems, and behavioral economics is fascinating. I also appreciate that Media.net has built genuine global scale from India, which is an inspiring story to be part of."

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Q2. Describe a project where you had to work with large amounts of data.

Sample Answer: "For my final-year project, I analyzed 6 months of e-commerce transaction data (approximately 2 million records) to identify purchase patterns using clustering algorithms. I used pandas for EDA, scikit-learn for K-Means clustering, and built a recommendation prototype. The most challenging part was handling data quality issues — missing values and inconsistent formats — which I resolved through a preprocessing pipeline. The project taught me the importance of data cleaning and the gap between textbook ML and real-world data."

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Q3. How quickly can you learn a new technology or framework?

Sample Answer: "I've always been a fast learner when I approach a new technology with a project mindset rather than passive studying. When I learned React.js, I built a portfolio dashboard within my first week and had a deployable app within two weeks. I find documentation + a small hands-on project + reading others' code on GitHub is my most effective learning stack. For whatever stack Media.net uses, I'm confident I can become productive within 2–3 weeks."

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Q4. Tell me about a time you improved the performance of a system or code.

Sample Answer: "In a college web application project, our API response time was 3+ seconds for a dashboard endpoint. I profiled it and found an N+1 database query problem — we were making one query per user record in a loop. I rewrote it using a single JOIN query with the ORM's select_related method, and added a Redis cache for frequently accessed data. Response time dropped to under 200ms. That experience made me love performance engineering."

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Q5. What do you know about real-time bidding (RTB) and AdTech?

Sample Answer: "Real-time bidding is the backbone of programmatic advertising. When a user loads a webpage, within milliseconds, an auction occurs where advertisers bid for the right to show their ad to that specific user. The process involves the publisher's ad server, supply-side platforms (SSPs), ad exchanges, and demand-side platforms (DSPs) — all communicating via OpenRTB protocol. Media.net operates on both sides of this ecosystem, making it a particularly interesting vantage point. I've read about header bidding, which reduces latency and increases publisher revenue by running auctions in parallel rather than sequentially — and I'm eager to understand how Media.net implements this at scale."

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Preparation Tips

• Master the LRU Cache and Sliding Window patterns: These appear frequently in Media.net's coding rounds and are directly relevant to ad serving systems. Make sure you can implement both cleanly from memory.
• SQL is not optional: AdTech runs on data, and Media.net values SQL proficiency highly. Practice complex queries — window functions (ROW_NUMBER, RANK, LEAD/LAG), CTEs, subqueries, and query optimization on Mode Analytics or LeetCode's SQL problems.
• Understand HTTP and REST APIs: Media.net's systems are API-heavy. Know HTTP methods, status codes, REST design principles, and basic authentication (JWT, OAuth). This frequently comes up in technical interviews.
• Learn the basics of AdTech vocabulary: RTB, DSP, SSP, CPM, CPC, CTR, viewability — understanding these terms shows genuine interest in the domain and impresses interviewers significantly.
• System design at the junior level: Be able to design a URL shortener, a basic rate limiter, or a simple caching layer. Media.net's Round 2 often includes a basic system design question for freshers.
• Strong OOPS fundamentals: Java and Python OOPS questions are common. Know inheritance, polymorphism, encapsulation, abstract classes vs. interfaces, and design patterns (Singleton, Factory, Observer).
• Build a portfolio of projects: Media.net strongly values candidates who have built real web applications, worked with APIs, or contributed to open-source. Your GitHub profile should have at least 2–3 strong, documented projects.

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Frequently Asked Questions

Q: What is Media.net's fresher package for software engineers in 2026? A: The CTC for Software Engineer roles typically ranges from ₹12 LPA to ₹22 LPA depending on the role (backend, ML, product), college tier, and individual performance. The package includes base salary, performance bonus, and ESOPs.

Q: Does Media.net offer ESOPs to freshers? A: Yes, Media.net typically includes Employee Stock Options (ESOPs) as part of the compensation package. Given the company's trajectory and potential IPO or acquisition upside, these can be significant in value.

Q: Which tech stack does Media.net primarily use? A: Media.net uses Java and Python extensively on the backend, with Go gaining traction for high-performance services. Frontend uses React. ML teams use Python with TensorFlow and PyTorch. The infrastructure is primarily AWS-based.

Q: How is the work culture at Media.net for freshers? A: Media.net is known for a strong engineering culture, relatively flat hierarchy, and genuine ownership at the junior level. Work hours are typically 45–50 hours per week, with some spikes during major product launches. The Mumbai office has a collaborative, startup-like atmosphere.

Q: Does Media.net hire freshers for Machine Learning roles? A: Yes, Media.net actively hires freshers for ML Engineering and Data Science roles. Candidates with strong Python, statistics, and ML project experience (recommendation systems, NLP, or ad relevance models) are preferred.

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Last updated: March 2026 | Source: Campus placement data, Media.net careers portal, engineering blog, student community reviews.