This Is Required To Put The Interviewer On Your Side

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

DSA Interview Step #2: Figure Out Your Overall Strategy

DSA Interview Step #1: Clarify The Problem

- The first step for any data structures and algorithms (DSA) problem is to *clarify the problem*. This means understanding exactly what the input and expected output are.
  - A common failure mode for candidates is that they will jump straight into coding, not doing this step. This leads to them wasting a lot of time writing code that isn't actually relevant.
- Often times in an interview, you will get a purposefully ambiguous problem - This happens with Big Tech a lot. The goal is get signal on the candidate's communication abilities.
- Clarifying the problem can help you *a lot* as it's very possible that the problem's scope is much more limited than you initially thought.

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

How Big Tech Judges Interview Performance Differently

What is the main extra axis that Big Tech companies will grade interview candidates on?

As long as you get a correct, optimal solution, that is enough to pass a Big Tech interview round.

Why is communication so important in Big Tech companies?

True or false: Big Tech companies don't really care about what specific technologies and programming languages you have worked with.

What's the core high-level asset Big Tech interviewers look for?

- At a lot of companies, you can just get the right solution and pass. This will often not be the case with Big Tech interviews.
- Communication is much more important at Big Tech companies as there's more stakeholders on everything
- Try not to be silent for 5+ minutes at a time during the interview. Regularly be explaining your thought process
- Massive companies like Meta and Microsoft will have very custom technology, so it doesn't really matter what you're currently experienced in
  - Because of this, Big Tech companies generally let you code in whatever language you want
- It's possible to get the sub-optimal solution or a slightly incomplete solution and still pass the interview if your communication is very strong

Related resources:
- [[Masterclass] How To Ace Your Big Tech Interview - Data Structures And Algorithms](https://www.jointaro.com/lesson/V4sTACzfOh22nMVcUV3w/masterclass-how-to-ace-your-big-tech-interview-data-structures-and-algorithms/) - The video this clip was taken from
- [Alex's Guide To Effective Communication](https://www.jointaro.com/playlist/hVdLQQzfslaYarLSCmmy/)
- [[Masterclass] How To Ace Your Big Tech Interview - System Design](https://www.jointaro.com/lesson/J3I9fTW9ulr6T9KjxpqY/masterclass-how-to-ace-your-big-tech-interview-system-design/)
- [How Alex Got Into Meta With 0 Prior LeetCode Experience](https://www.jointaro.com/lesson/ZYiTbN21FsqFxFdpwt5a/how-alex-got-into-meta-with-0-prior-leetcode-experience/)

Truly Mastering A Data Structures And Algorithms Problem  - 2 Sum Example

Why is the following not a good way to remember the solution to 2 Sum?
        
def twoSum(self, nums, target):
    required = {}
    for i in range(len(nums)):
        if target - nums[i] in required:
            return [required[target - nums[i]],i]
        else:
            required[nums[i]] = i

Why is [Look for the difference and use a HashSet] a great way to remember the solution to 2 Sum?

What more general higher-level learning principle is also exercised alongside the proper mastery of data structures and algorithms problems?

Data structures and algorithms are a core part of FAANG software engineer interviews, and they're seemingly the bane of every software engineer's existence. However, so many software engineers don't learn these concepts properly.

Here are the core points from the video:
- In this example, we cover 2 sum, a classic data structures and algorithms problem that many companies use on phone screens. Here's how to learn it properly:
  - **Bad:** Memorizing all the code behind the optimal implementation.
  - **Good:** "Look for the difference and use a HashSet."
- All DSA problems will have this core insight that "unravels" the entire thing. Once you find and truly understand this insight, the implementation should flow naturally.
- This is also a sign of higher learning via "thinking in terms of abstractions". Your brain is a lot more powerful if it is able to take something more singular and high-level and convert it into the nitty gritty. Memorizing everything at the lowest level simply isn't scalable.

Related resources:
- [[Masterclass] How To Ace Your Big Tech Interview - Data Structures & Algorithms](https://www.jointaro.com/lesson/V4sTACzfOh22nMVcUV3w/how-to-ace-your-big-tech-interview-data-structures-and-algorithms-7172021/) - This is the full video this clip is taken from
- [[Masterclass] How To Ace Your Big Tech Interview - System Design](https://www.jointaro.com/lesson/J3I9fTW9ulr6T9KjxpqY/how-to-ace-your-big-tech-interview-system-design-1162021/)
- [How Alex Got Into Meta With 0 Prior Leetcode Experience](https://www.jointaro.com/lesson/ZYiTbN21FsqFxFdpwt5a/how-alex-got-into-meta-with-0-prior-leetcode-experience/)

Effectively Learning Data Structures And Algorithms (DSA) For Software Engineer Interviews

Which of these is the core building block of data structures and algorithms problems?

True or false: The most effective way to be prepared for data structures and algorithms interview problems is to memorize the code of each problem's solution line by line.

Why should you comment your solutions to data structures and algorithms problems?

True or false: When you are completely stuck, you should just look at the entire solution in order to learn it.

Why is it healthy to struggle on data structures and algorithms problems?

**Data Structure and Algorithm (DSA)** questions are a core part of the interview for most software engineering roles. These questions, sometimes called "Leetcode problems", are essential to master if you want a reasonable pass rate in your interviews. 

However, so many engineering candidates struggle to truly understand these questions, resulting in lower confidence and worse performance. Here are the best tips when it comes to passing these types of interviews:

- The key to mastering DSA is to learn patterns and not memorize code. The "drill and kill" style of studying is really badly suited for DSA.
- 80% of asked DSA problems will use 1 of ~15 different patterns. So rather than focusing on individual problems, your goal should be to connect the problem to a broader, recurring pattern.
- Examples of patterns: binary search, graph traversal, sliding window, tail recursion. Being able to understand problems of the same type is essential. Being able to *name the pattern* is even better. 
- Extensively comment your solutions, primarily with the key pattern behind the problem. When you get started, it will be painstakingly slow to understand each line of code, and potentially comment it. That's ok! You will feel frustrated, but your goal is to deeply understand the problem and solution, instead of simply solving as many problems as you can.
- When you can't figure out a problem, read 10-20% of the solution and see if you can come up with the entire solution from there. Build the muscle of converting high-level points into fleshed out solutions

Related resources:
- ["Mock Interviews vs. Leetcode?"](https://www.jointaro.com/question/U4kohSE8g5LR7GD9cqYH/mock-interviews-vs-leetcode/)
- [[Masterclass] How To Ace Your Big Tech Interview - Data Structures And Algorithms](https://www.jointaro.com/lesson/V4sTACzfOh22nMVcUV3w/masterclass-how-to-ace-your-big-tech-interview-data-structures-and-algorithms/)
- [[Masterclass] How To Ace Your Big Tech Interview - System Design](https://www.jointaro.com/lesson/J3I9fTW9ulr6T9KjxpqY/masterclass-how-to-ace-your-big-tech-interview-system-design/)
- [How Alex Got Into Meta With 0 Prior LeetCode Experience](https://www.jointaro.com/lesson/ZYiTbN21FsqFxFdpwt5a/how-alex-got-into-meta-with-0-prior-leetcode-experience/)

The Best Programming Languages For Interviews

- Try to use a more concise language like Python if possible. This is because interviews, DSA rounds in particular, are a precise time management exercise.
- However, if you are *very* familiar with a certain language, use that. The exception is if that language is very verbose like C.
- Avoid the trap of using a rarer, "fancier" language. It may cost you time to explain, and if you aren't actually that knowledgeable in it, you may get called out.

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

This Tool Is Especially Amazing For Big Tech Interviews

What Big Tech Data Structures And Algorithms Interviews Generally Look Like

FAANG interviews are some of the most well-defined in the industry. However, this style of software engineer interview is often completely failed due to a lack of preparation. Here's what to expect when it comes to data structures and algorithms interviews for Big Tech.

Here are the core points from the video:
- **Phone screens are Leetcode easy and medium** - This will either be 2 easy problems or 1 medium problem. This means that if you haven't progressed to onsite rounds yet, don't spend much time on Leetcode hard problems: That's a premature optimization. Make sure you have your easy and medium problem mastery down, so you can even make it to that final round.
- **Onsites are Leetcode medium and hard** - This will either by 2 medium problems or 1 hard problem. If you receive a problem that's more on the medium side, make sure to budget your time well. There's a good chance the interviewer wants you to solve another problem on top of that.

Related resources:
- [[Masterclass] How To Ace Your Big Tech Interview - Data Structures & Algorithms](https://www.jointaro.com/lesson/V4sTACzfOh22nMVcUV3w/how-to-ace-your-big-tech-interview-data-structures-and-algorithms-7172021/) - This is the full video this clip is taken from
- [[Masterclass] How To Ace Your Big Tech Interview - System Design](https://www.jointaro.com/lesson/J3I9fTW9ulr6T9KjxpqY/how-to-ace-your-big-tech-interview-system-design-1162021/)

The #1 Tried And True Tool For DSA Interviews

Related resources:

- [\[Taro Top 10\] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [\[Taro Top 10\] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

Having The Proper Mindset For Interviews

True or false: You need to care a lot about your Big Tech interview, because passing it will drastically improve your life. The more prominent it is in your brain, the more likely you are to pass.

True or false: Your input is much more important than the output and results you achieve.

Why should you not view interviews as a test?

True or false: You shouldn't take any breaks when studying for interviews, because the relentless focus will help you learn the material better.

- *The more you care about your interview, the less likely you are to pass it.*
- Interviews are all about maintaining a nimble, flexible state of mind, so you can react gracefully when hit with a problem you haven't prepared for. This will almost always happen on interviews with tougher companies.
- If you view your interview as a "make or break" opportunity (which it admittedly very well may be) and go into it with a lot of anxiety, you will not have that healthy state of mind. When your interviewer goes off-script, your mental state will likely shatter.
- View interviews as a chance for you to go in and learn from some very smart people, and if you happen to get a job offer, that's a nice bonus. This much more relaxed and positive mentality will reduce your anxiety.

Related resources:

- [\[Taro Top 10\] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)
- [\[Masterclass\] How To Ace Your Big Tech Interview - System Design](https://www.jointaro.com/lesson/J3I9fTW9ulr6T9KjxpqY/masterclass-how-to-ace-your-big-tech-interview-system-design/)

Avoid This Common Bad Habit That Fails You Interviews

You should allocate enough time to prepare for your data structures and algorithms interview to avoid cramming at the last minute.

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

Hate Data Structures And Algorithms? Turn It Into A Habit

Set aside 30 minutes, 2-3 times a week, to build a regular habit out of learning data structures and algorithms.

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

Interview Preparation Is Way Better With Others

Here are the core points from the video:
- Social accountability is a good way to motivate yourself to work hard when studying data structures and algorithms, especially given how miserable studying them can be. Peer pressure works!
- Doing a mock interview with a friend can help you prepare for interviews because you are forced to explain yourself out loud. This process will be more akin to the actual interview process, far more than grinding LeetCode at your computer all day.

If you're looking for a mock interview partner, find one with [Taro Networking](https://www.jointaro.com/networking/): [https://www.jointaro.com/networking/?selectedServices=Mock+Interviews](https://www.jointaro.com/networking/?selectedServices=Mock+Interviews)

Related resources:
- [[Masterclass] How To Manage Your Time Optimally In Tech And Achieve More Results](https://www.jointaro.com/lesson/rnX9EKpzc43TgGW9MozG/masterclass-how-to-manage-your-time-optimally-in-tech-and-achieve-more-results/) - This is the time management session Rahul mentions
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

The Amazing Non-Money Benefits Of Working At Big Tech

DSA Prep Is One Of The Highest ROI Activities On Earth

If you do a good job studying for your data structures and algorithms interview, you can set yourself up financially for the next few years.

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

Struggling With DSA? Don't Worry, That's Healthy!

True or false: Interviewing is all about how much you know. As long as you study a lot and understand enough material, you will pass the interview.

For almost everyone, what does the learning curve look like for data structures and algorithms?

True or false: It's completely normal and even a good sign when you feel dumb while studying data structures and algorithms.

What's the most important piece when you're learning data structures and algorithms material?

True or false: If you aren't making progress on data structures and algorithms after 2 months, your mind probably just isn't geared to understand them. At that point, you should be practical and just look for interviews that don't have these kinds of problems.

Mastering data structures and algorithms (colloquially known as "DSA") is a frustrating experience: It requires such a different type of thinking and is rarely required on the actual software engineering job. This is why if you want to get good at DSA, you need the right mentality first.

Here are the core points from the video:
- DSA learning is an exponential process. You are going to be terrible initially, and eventually things will start to "click" once you hit the inflection point.
- When you feel dumb, understand that this is completely natural: This is almost a good thing as it's entirely expected.
- DSA is very much an input-driven exercise. Build a good process around studying it regularly and commit to it. With constant persistence, you will eventually get to where you want to be!

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

How You Can Supercharge Your Team - Discovering Synergies

What Does A High Trust Team Mean For Code Review?

In this closing section, we focus on the importance of **building and operating within a high-trust engineering team**. Trust is not just a feel-good concept—it directly affects our productivity and velocity when shipping code.

- **We prioritize speed and trust in code review.** When we trust our teammates, we don’t block diffs unnecessarily. Instead, we approve with minor suggestions, knowing the author will follow through responsibly.
- **We use “accept with nits” to keep things moving.** For small, straightforward changes—like renaming variables—we leave a “nit” comment but still approve the diff to avoid introducing unnecessary delay.
- **We avoid slowing each other down over trivial issues.** Each extra cycle of “request changes” can delay code by 1–2 days. We conserve team momentum by trusting competent authors to resolve minor feedback themselves.
- **We treat time as our most precious resource.** Code review should add value without becoming a bottleneck. High-trust practices help us operate with both quality and speed.
- **We maintain expectations through communication.** Even when approving with nits, we clarify, *“Please address before landing,”* so everyone stays aligned on expectations without blocking forward motion.

Code Review MUST Be Fast, Otherwise The Team Is Unhealthy

In this final segment of the video, the speakers reflect on the crucial role that **code review turnaround time** plays in engineering culture, team productivity, and developer happiness. Timely reviews are not just a technical necessity—they reflect the team’s values and operational discipline.

- **We treat code review turnaround time seriously.** Our standard should be to review diffs within *one day*, two at most. Slow feedback cycles—especially taking 4–5 days—compound and slow down overall delivery velocity.
- **We recognize code review as a gatekeeper with real power.** Since all production code must pass through this step, bottlenecks here can deeply affect morale, productivity, and team trust.
- **We advocate for clear team expectations.** When reviews consistently lag, we push for a shared SLA (Service Level Agreement) around review times, ensuring accountability and smoother planning.
- **We balance urgency with respect.** While prompt reviews are key, we also avoid pestering reviewers within minutes of submitting. Giving at least 6 hours (unless it’s urgent) is a sign of maturity and respect.
- **We proactively build good habits.** Many of us block time at the end of our day to batch and respond to review requests, which helps us maintain velocity without excessive context-switching.

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