Please provide correct answers to the below interview transcript:

Interviewer: Yeah. So I'll be, let's just go through some basics. Okay? And we'll also do a 
python algorithm quesƟon. Okay, you are in your laptop. Okay?
 
Interviewer: alright good. Good. Yeah. Okay, so and we'll also cover some concepts. 
Yeah, you did menƟon, let's start from whatever you menƟoned in your resume, which is the 
Pdf Chat port. Right? So basically, the from what I understood. 
Interviewer: 
Okay? Which one did you use? 
 
Interviewer 
And for conversion of, you know, the chunks into embeddings, what did you use. 
 
Interviewer 
Yeah, the same number of vectors. 
So and also in vector search. So you menƟoned, I think you were trying to menƟon semanƟc 
search. Right? 
Yeah, okay? So for like, in vector search, okay, there and vector uses different techniques 
right to search. 
 do you recall anything like we call it as measuring the distance. 
Interviewer 
Yeah, it's like, it's scalars and vectors. Right? But background. But okay, then I'll rephrase my 
quesƟon.
 So have you heard of Euclidean? 
Interviewer 
okay, and what is cosine similarity? 
Interviewer 
okay, that's fine. Yeah. But yeah, good to. It's I think it's a good idea to get to know these 
things because you're dealing with vectors. Right? 
Yeah. 
Interviewer 
Yeah, I got a preƩy good idea of overview of the project. So that's good.
Interviewer 
yeah. 
And you did menƟon FAISS and Pinecone. Yeah, that's good. Let's come back to LLM. So I 
want to use like an LLM, right? Like, for example, you use 2 digital phone. 
 
Interviewer 
So if I want to use some other LLMs also like, but or something or 
yeah, so there are some techniques by which we can opƟmize the allowance performance.
So there are some techniques. 
Okay. to opƟmize the eleventh performance
 
Interviewer 
Can you briefly explain model pruning quanƟzaƟon
Interviewer 
okay. and do you know model pruning. You menƟoned model pruning
 
Interviewer 
so let's take a scenario. Okay? So I know your Pdf standpoint. That's one thing. Right? Let's 
do one more. Okay. Now, you are the a ML developer. Okay for a new project. 
Okay? The project is legal document analysis. 
 
Interviewer 
okay, so we are, basically, it's similar to whatever you did. Okay, it's in the same context. So 
you don't need to worry. You already know the answer kind of. But the thing is the difference 
here is so for okay, I'll ask you this 
in your project. Which documents did you use 
you said documents? Right? 
Interviewer 
so. But what what is the content 
like? I have used normally like Pdfs, which is random there like a medical here, here, here. It 
would be legal documents right now, if you are using the same Pdf, if you are building the 
same Pdf, Chatbot again. Now for these documents 
is your current project. Whatever you have done is that enough? Or you want to add some 
addiƟonal, you know processing, and you want to take care of some things.
How do you opƟmize it?
 
Interviewer 
Yeah, that's no, no, no, Ij you can use 3.5 fine. I don't mind about that. But I am more 
worried about in legal documents. We have confidenƟal data. We have sensiƟve data. So 
how do we handle that right? So we need to have like, we don't need to comp. We should 
not compromise on accuracy. 
Interviewer 
so what comes to your mind like to handle the sensiƟveness and confidenƟality?
What can we do to improve our project? 
Interviewer 
Okay, anything else. 
forget the processing. Any other. 
Interviewer 
Do you have experience with LangChain? 
Why do we use it?
Interviewer 
But have you done any Poc your personal any? Did you try to use LangChain in any of your 
project Ɵll now?
Interviewer 
Okay, then how did it help you? If you. Let's take 2 scenarios. Okay? So one is, you use 
LangChain, which is your current scenario, and one is where we don't use LangChain. Okay. 
what are the advantages and disadvantages. Do you see equipment for using LangChain? 
Any special feature LangChain provides 
Advantages and disadvantage of using LangChain, and advantages and disadvantage of not 
using LangChain. 
Interviewer 
Anything else 
Interviewer 
Yeah, let's come to NLP, like you menƟoned tokenism? Right?
What is a tokenizer? 
What is the tokenizaƟon process?
Interviewer 
and what are the advantages like? Okay, let's start with the definiƟon. Yeah, we'll go from 
there. 
Interviewer 
okay, any disadvantages in tokenism 
Interviewer 
Okay? So basically, we might, we might lose the context. 
Interviewer 
okay. And what is Lemma? Have you heard of lemmaƟzaƟon stemming?
 Interviewer 
So in which place in which situaƟon should I use lemmaƟzaƟon? And in which situaƟon 
should I use stemming? 
Interviewer 
okay? And which. So so, okay, so you think like is, LemmaƟzaƟon is more accurate. Right? 
I have one quesƟon follow up is, which is more fast LemmaƟzaƟon or stemming? 
 
Interviewer 
Got it? Got it? So we'll come back to Nlp. Now. Okay, so 
I hope you know TF-IDF. How does it work 
Interviewer 
okay. And we have word embeddings right? Like word to it. Have you heard of word2vec? 
So what does that do? 
 
Interviewer 
alright. 
So and do you know, any embedding techniques? We have mulƟple embedding techniques.
Interviewer 
okay 
in RAG. Okay, so is that the extent of knowledge you have on RAG? Or can I ask some? 
Follow up quesƟons? 
l be answered at the Ɵme.
Interviewer 
Okay, okay, yeah. I mean, that's that makes sense. Yes, yes, okay. 
and 
let's come to ML 
 good. You did menƟon quanƟzaƟon and stuff. So
 
Interviewer 
let's say, we have an ML project. We have imbalanced data sets. 
 how do we handle imbalance data sets? 
 
Interviewer 
alright any other. 
Interviewer 
So, and if we have a ML model. 
do we have any techniques to opƟmize them?
Interviewer 
like. 
do you know any like? So, for example, there is something called as hyper parameter tuning. 
Right? 
Interviewer 
how? How like, do you know, do you have any idea on deep learning. We have python 
frameworks to do work on deep learning. Right. Do you know any python frameworks? 
Interviewer 
okay, let's come back. I want to also know one more thing. What is your current project in 
your current company like, 
Interviewer 
Okay, how do you do data collecƟon
Interviewer 
okay. Then, aŌer that, what happens
 
Interviewer 
Okay? No, that's fine. That's fine. 
Let me. I'm just going through the project 
that you have menƟoned in the cv, there is anything that is interesƟng.
Okay, so you are mostly analyzing the data. Right? This is a sales data from oracle. Okay, 
okay. 
so aŌer you get this, all of this distant train, then they will predict the buybox price, and all 
of that. The rest of the agriculture conƟnues. Yes.
okay. Are you confident in wriƟng code and all like, do you write code regularly? Or it's 
mostly tool work. Do you write python code every day? And you are confident, right? 
Do you use GPT to prompt? Or do you write the funcƟon?
Interviewer 
correct. Let's go to some other things. Now. Okay, have you heard of NER? 
Named EnƟty RecogniƟon? It's called NER. 
okay. And what does it do? Where do we use it? 
Interviewer 
yeah. Yeah. But what I use, I mean. 
Interviewer 
Yeah. 
okay, 
let's do one more scenario. What do you know about Bert? 
Interviewer 
Yeah. 
Okay? Or or let's do like this. 
Forget BERT. Okay. 
another scenario. 
We are creaƟng the chatbot. 
 We are in a bank. So it's a chat board for a bank. 
 
Interviewer 
So all financial documents, right? 
 But I have a problem. The problem is, my documents are coming from various sources. 
 
Okay, like, because it's a bank, right? It's connected to mulƟple departments.
Technically, okay, not management department. Okay? So we have you, you know, Jira, 
right? Jira, because. 
okay, and we have data. 
Interviewer 
okay. So we are in a bank, and we want to build a chat port. Right? 
Interviewer 
But the data is coming from different sources. 
Interviewer 
so one of the sources database. Obviously. 
Interviewer 
Another source is Jira Ɵckets.
Jira Ɵckets. And I have applicaƟons deployed in producƟon also. So I have locks, right locks, 
locks, applicaƟon locks.
Interviewer 
and calls. And website. They will post lot of arƟcles right bank people. 
 
Interviewer 
how to get all this data first. Right? So there is a process right? As a, if we are doing this 
project. So step one, we need to take this data step 2, we need to do something. Step 3. We 
need to do something. Finally, my chat port is ready. 
So I want you to explain me. Well, those steps 
 
Interviewer 
yeah, got it. So then your chat board should be able to answer it in. In all this process. Which 
do you think? Will be like? LiƩle difficult to do? Which part which step
it may be complex, which step is more complex. 
Interviewer 
Okay? And how do you do it like? Let's g take some hypotheƟcal like, go on like and give me 
some more detail. 
How do you train the model? And how do you evaluate 
Interviewer 
Got it. Okay? And once you save it, you have the knowledge ready knowledge base, ready? 
Interviewer 
got it. Yeah. But you missed one thing, right? EvaluaƟon. You said some evaluaƟon. We need 
to do also. 
Interviewer 
Hmm, alright. So 
do you know any machine learning algorithms? I think you said Random Forest, right? 
 
Interviewer 
Yeah. Got it, you understand? So. But this random forest it seems like very popular right? 
Everybody uses any idea it any idea how it works internally 
Interviewer 
got it. Yeah. Makes sense. Yes, alright. So 
okay, let's do some coding. Now, okay, so can you share your screen and open any id like. 
 
Interviewer 
Can you also share your screen 
 
Interviewer 
Listen to what I’m saying and write a python program 
Interviewer explains quesƟon
 
Interviewer 
Can you explain what's the logic 
Let's go through the code. Okay, so what is the logic that you wanted to do? 
 
Interviewer 
No, no. You don't need to explain line by line. 
 
Interviewer 
I'm just worried. I'm just, I just want to know what is happening in the for loop. 
Interviewer 
where you are keeping. Track 
Interviewer 
alright like, Okay, why do you think it is not working what seems to be the issue? 
feel like? This is too complex. There are very easy soluƟons for what I asked.
 We want to improve or 
you want to improve and try to get the correct answer, 
I'll give you 2 2 min only. Okay. 
yes, yes. 
thanks. 
Interviewer 
that was the last quesƟon. That was all. The you can stop sharing.
Yeah. So 
do you have any quesƟons for me?
 
Interviewer 
in the concept, size and all. I think you are okay overall, But you need to have more hands 
on, I think, based on more hands on experience, you will be geƫng more like RAG, for 
example, and LangChain, and all beƩer to have more hands on that would help. But I am. I 
understand that your project has no requirement in that. 
 
Candidate 
This is a very like that. 
like I was interested in LLM. Like that when it got the concept got introduced to me like bit. 
Then I got interested in that. I got. I only just done research, and I have done hands on Pdf, 
one. I have done one taken one small Pdf, and done like that. 
Interviewer 
Yeah, I understand that. I understand that, you know. Yeah, that's that's great to know. Keep 
keep, you know, doing more applicaƟons, personal applicaƟons.
Interviewer 
that would give you more exposure in to different libraries.