AI & ML Interview Preparation Resource — Free PDF by Md Aniq Over the past few weeks, I’ve been revising and organizing the most important concepts in Artificial Intelligence, based on what top companies frequently ask during technical interviews. To help others preparing for AI/ML/Data Science roles, I created a clean and crisp PDF containing: 📘 Top 50 Most Frequently Asked AI Interview Questions 🔹 Covers fundamental to advanced concepts 🔹 Suitable for beginners to experienced professionals 🔹 Helps in quick revision and interview confidence 🔹 Curated personally based on real interview experience I’m sharing it openly so the community can benefit. Feel free to download, practice, and share it with anyone preparing for AI roles. 👉 50 AI Interview Questions – PDF Let’s keep learning and growing together! 💙 #ArtificialIntelligence #MachineLearning #DeepLearning #DataScience #AIInterview #InterviewPreparation #LearningEveryday #MdAniq #TechCommunity

AI won’t replace engineers any time soon…but engineers who use AI will replace those who don’t. Here’s how top UK candidates are using AI in interviews and on the job: Generating code snippets to speed up delivery Writing unit tests in seconds Spotting bugs before they hit production The secret? Don’t let AI do the thinking for you. Use it to accelerate your workflow and free up brainpower for solving bigger problems. 👉 Watch this video to see how AI can help you code smarter, test faster, and stand out in interviews.

AI as an interviewer? I had an AI interview yesterday, and honestly, it felt surprisingly close to talking to a real person. The whole thing felt slightly surreal. The system had divided the interview into four sessions of around ten minutes each. The first two were based on work scenarios and technical skills. These parts felt the most natural because the AI seemed to understand the context and respond in a way that made sense. But things shifted when it moved into logical thinking and behavioral questions. This is where the AI felt more like a wall than a conversation partner. You cannot really understand someone’s behavior or personality just by matching certain words or patterns. That part made it clear that human insight is still necessary. AI can evaluate structure, clarity, and logic, but it cannot replace the depth of a real human reading your tone, your intent, and your emotional cues. From this experience, I feel like the best approach would be a mix: AI for the technical and structured parts, and humans for the behavioral side. That balance would actually make the interview process better, not worse. It was also interesting seeing this from the perspective of someone who has worked on AI training projects. It almost felt like I was talking to something I had helped build, in some small way. A strange but amazing feeling. #AI #humanresources #aihuman

I use AI in my recent job searches and to analyze my interview feedback. I was reviewing the recorded conversations word by word with AI, but it started hallucinating. For example, when the result was actually a job rejection, it predicted more than an 80% chance of getting an offer. A word-by-word interpretation can be very different from real human decision-making. How has your real interaction with AI been, and what was your experience? Do you think AI can be used for more than just a chatbot? Share your thoughts here!

You are appearing for an Applied Scientist interview and interviewer asks: Interviewer: “What is the intuition behind Residual Connections?” Candidate: “It helps gradients to flow through.” ❌ Vague and Generic. Want to stand out? Say this instead: “It makes the identity function the default and only learns the delta on top of it.” That one line tells them you get it. Check out the bite sized slides for detailed intuition. 👇 🚀 Follow @𝗦𝗼𝘂𝗺𝗶𝗸 𝗗𝗮𝘀𝗴𝘂𝗽𝘁𝗮 for weekly explainers that make you interview-ready and industry-sharp. #MachineLearning #DeepLearning #ResNet #AI #InterviewTips #Transformers

Interview prep with AI isn't cheating, if done correctly. It's training smart, like shadowboxing before a real fight. The trick? Stop memorizing answers. Start anticipating patterns. #UsingAIWithIntent (Part 3)

There’s a new breed of AI agents and they’re starting to fool even the savviest of hiring managers. Interview AI assistants, like Parakeet AI, InterviewCoder, and Leetcode Wizard, are designed to help candidates prep by giving instant tips, answers, and feedback. Lately, some coders have taken it too far, using these tools not just for practice, but secretly during real interviews to get answers in real time. This turns interviews into a guessing game, where hiring managers, HR teams, and senior leaders may not realize they’re being outsmarted by AI. If misused, these agents risk damaging trust and making it harder for honest candidates to shine. Even if hiring managers ask candidates to share their screens, these AI assistants can still be used on a phone at the same time. How do you think interviewers should handle this new challenge? #HR #Interviews #AIAgents https://lnkd.in/d6GrTiiH

🧠 Should interviews test how people think with AI? Most hiring still measures what candidates know, not how they work with AI to solve problems. Imagine a short-lived AI prototyping round — a 30-minute session where a candidate uses AI to explore, iterate, and reason through a real challenge. Would this reveal stronger thinkers? Or just add complexity to hiring? Curious to hear what you think. #AI #Hiring #FutureOfWork #Recruiting #ProductManagement

You’ve trained the models. You know the algorithms. But can you talk about them in an interview? AI/ML Engineer interviews test more than technical knowledge, they test how you think, explain, and solve real problems. Here’s how to structure your answers, highlight your hands-on projects, and use the STAR method to stay clear under pressure. Read: Cracking the AI/ML Engineer Interview — What to Expect and How to Prepare 👉 https://lnkd.in/egTu98Fm Not for those hoping to wing it with memorised answers. #SkillUpOnline #AI #MachineLearning #CareerGrowth #LearnAI #LevelUp

AI for user research doesn't mean replacing interviews. It means doing them smarter. Use AI to analyze interview transcripts, spot patterns across all the conversations, and surface the real pain points you might've missed. Tools like Claude or ChatGPT can pull themes in seconds. Real workflow: → Record interviews → Transcribe (ChatGPT can do this) → Get a synthesis of top 5 user problems → Validate with your team. This cuts research analysis time by 60% and surfaces insights faster than manual note-taking ever could. How are you currently analyzing user feedback?