Google vs Salesforce: Interview Question Comparison

When preparing for technical interviews, understanding company-specific patterns is crucial for efficient study. Both Google and Salesforce ask questions covering core data structures and algorithms, but they differ significantly in volume, difficulty distribution, and focus. This comparison analyzes their question banks to help you strategize your preparation.

Question Volume and Difficulty

The most striking difference is scale. Google's repository is over ten times larger, with 2217 questions compared to Salesforce's 189. This volume reflects Google's broader hiring needs and the extensive historical data available from candidates.

The difficulty distribution also reveals distinct profiles:

• Google (E588/M1153/H476): The majority of questions are Medium (52%), with a substantial Hard portion (21.5%) and a significant Easy base (26.5%). This indicates a strong focus on complex problem-solving and optimization, even in early screening stages.
• Salesforce (E27/M113/H49): The distribution skews heavily toward Medium (60%), with a smaller proportion of Hard (26%) and Easy (14%) questions. This suggests their process is highly concentrated on intermediate-level algorithmic challenges, with fewer extremely simple or convoluted problems.

In essence, Google's interview has a wider difficulty range and a higher absolute number of challenging problems. Salesforce's process appears more targeted, with a clear emphasis on mastering medium-difficulty concepts.

Topic Overlap

Both companies emphasize the same four fundamental topics: Array, String, Hash Table, and Dynamic Programming. This overlap means core preparation in these areas serves both interview processes well.

• Arrays & Strings: These are the bedrock. Expect questions on two-pointer techniques, sliding windows, and string manipulation.

# Two-pointer example: Reverse a string in-place (Python)
def reverse_string(s):
    left, right = 0, len(s) - 1
    while left < right:
        s[left], s[right] = s[right], s[left]
        left += 1
        right -= 1

• Hash Tables: Critical for optimization problems involving frequency counting, lookups, and memoization.
• Dynamic Programming: A key differentiator for harder problems. Master classic patterns like knapsack, longest common subsequence, and DP on strings/arrays.

The main difference lies in depth and context. Google's larger question bank often explores more nuanced variations and optimizations within these topics, while Salesforce's questions may relate more closely to business logic or data processing scenarios common in enterprise CRM environments.

Which to Prepare for First

Start with Salesforce. Its focused, medium-dominant question bank allows you to build a strong, efficient foundation in the core overlapping topics. Achieving proficiency here ensures you are well-prepared for the majority of questions you'll encounter in their process. You can realistically work through a significant portion of their tagged problems.

Then, transition to Google. Use the extensive question bank to stress-test your knowledge, tackle harder variations, and improve your speed and adaptability. The volume requires a longer, more dedicated study period. Preparing for Google second will feel like expanding your skill set to cover edge cases and higher complexity, building directly on the foundation you solidified for Salesforce.

This sequential approach—mastering the focused intermediate set before tackling the broad, advanced set—is the most efficient path to being prepared for both.

For detailed question lists and patterns, visit the company pages: Google and Salesforce.