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AIMonk Interview Questions and Answers

Updated 10 Dec 2024

AIMonk Interview Experiences

Popular Designations

4 interviews found

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
-
Result
-

I was interviewed in Oct 2024.

Round 1 - Assignment 

We were asked to created an application on local system using yolo for object detection and use container as well.

Round 2 - Technical 

(4 Questions)

  • Q1. Decorators in python
  • Ans. 

    Decorators in Python are functions that modify the behavior of other functions.

    • Decorators are defined using the @decorator_name syntax before a function definition.

    • They can be used to add functionality to existing functions without modifying their code.

    • Common use cases include logging, timing, and access control.

    • Example: @staticmethod decorator in Python makes a method static.

  • Answered by AI
  • Q2. Dropout, batch normalization
  • Q3. Ensemble methods
  • Q4. Tensorflow basic questions were asked. About custom model and custom dataset.

Skills evaluated in this interview

Machine Learning Engineer Interview Questions asked at other Companies

Q1. Find Permutation Problem Statement Given an integer N, determine an array of size 2 * N that satisfies the following conditions: Each number from 1 to N appears exactly twice in the array. The distance between the second and first occurren... read more
View answer (1)

Data Analyst Interview Questions & Answers

user image Siddharth Pandey

posted on 10 Dec 2024

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. What is accuracy in ml
  • Ans. 

    Accuracy in machine learning measures how often the model makes correct predictions.

    • Accuracy is the ratio of correctly predicted instances to the total instances in the dataset.

    • It is a common evaluation metric for classification models.

    • Accuracy can be calculated using the formula: (TP + TN) / (TP + TN + FP + FN), where TP = True Positives, TN = True Negatives, FP = False Positives, FN = False Negatives.

    • For example, if ...

  • Answered by AI
  • Q2. What is recall in ml
  • Ans. 

    Recall is a metric in machine learning that measures the ability of a model to find all relevant cases within a dataset.

    • Recall is calculated as the ratio of true positive cases to the sum of true positive and false negative cases.

    • It is also known as sensitivity or true positive rate.

    • A high recall value indicates that the model is good at identifying all relevant cases, even if it means more false positives.

    • For example,...

  • Answered by AI

Skills evaluated in this interview

Data Analyst Interview Questions asked at other Companies

Q1. Suppose there is a room in the office and X people enter room throughout the day, Y people leave throughout the day [continuously people are entering the room, some are staying there, and rest are going out] .. so tell me the code to calcul... read more
View answer (11)
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
Selected Selected

I applied via LinkedIn and was interviewed in Mar 2023. There were 2 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - Technical 

(2 Questions)

  • Q1. Basics in python
  • Q2. Basic and advanced concepts in ML

Machine Learning Engineer Interview Questions asked at other Companies

Q1. Find Permutation Problem Statement Given an integer N, determine an array of size 2 * N that satisfies the following conditions: Each number from 1 to N appears exactly twice in the array. The distance between the second and first occurren... read more
View answer (1)
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
-

I was interviewed before Apr 2023.

Round 1 - Assignment 

Assignment given to you over mail.

Round 2 - Coding Test 

Python basic coding test

Round 3 - Technical 

(1 Question)

  • Q1. Technical indepth machine learning theory question related to project done in your past company

Machine Learning Engineer Interview Questions asked at other Companies

Q1. Find Permutation Problem Statement Given an integer N, determine an array of size 2 * N that satisfies the following conditions: Each number from 1 to N appears exactly twice in the array. The distance between the second and first occurren... read more
View answer (1)

AIMonk interview questions for popular designations

 Machine Learning Engineer

 (3)

 Data Analyst

 (1)

Interview questions from similar companies

I was interviewed in Jun 2017.

Interview Questionnaire 

3 Questions

  • Q1. Tell me about about yourself?
  • Ans. 

    I am a passionate Machine Learning Engineer with a background in computer science and a strong interest in AI technologies.

    • Background in computer science

    • Experience in machine learning

    • Passionate about AI technologies

  • Answered by AI
  • Q2. Tell about your academic or your hands on projects in which you are involved?
  • Ans. 

    I have worked on projects involving natural language processing, computer vision, and predictive modeling.

    • Developed a sentiment analysis model using NLP techniques

    • Implemented a facial recognition system using computer vision algorithms

    • Built a predictive model for customer churn prediction

  • Answered by AI
  • Q3. Expected CTC?

Interview Preparation Tips

Round: Resume Shortlist
Experience: This round was "General Introduction Round" in which we have to introduce about our self-tell about hobbies and our family background and some lines about our hometown.
Tips: Just be confident and speak loudly and boldly.

Round: Test
Experience: It was online coding round via skype in which 5 questions were given out of which we have to do any 2.
Tips: "More you will practice more you will learn".
Duration: 20 minutes
Total Questions: 5

Round: Technical + HR Interview
Experience: This round was a combo with both technical and job questions. Questions were asked from my resume, basics questions in technical, general definitions and major questions from my project.
Tips: Specifically for Engineers searching for Data Scientist or Machine Learning based jobs, focus on your projects have done and do involve in the projects either on GitHub or haggle.
Clear your basic concepts and have a good knowledge of programming language like Python, R etc.

College Name: IIIT Bhubaneswar
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Referral and was interviewed in Jun 2024. There were 2 interview rounds.

Round 1 - Coding Test 

Focus more on python funda,ental and spark

Round 2 - Assignment 

They will ask more on datarbcisk related stuff

Interview Preparation Tips

Interview preparation tips for other job seekers - prepare well on python and prepare databricks adf and devops
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Naukri.com and was interviewed before Jun 2023. There was 1 interview round.

Round 1 - Coding Test 

4 technical questions, 1 python code, 2 SQL, 1 Spark

Interview Preparation Tips

Topics to prepare for Tiger Analytics Machine Learning Engineer interview:
  • Spark
  • SQL
  • Python
  • Hadoop
  • Hive
Interview preparation tips for other job seekers - Be confident and good in your skills
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via LinkedIn and was interviewed in May 2023. There were 3 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - Coding Test 

It was 1 hour coding test with 2 questions. One was easy and another was medium level coding question.

Round 3 - Technical 

(3 Questions)

  • Q1. Few SQL Queries
  • Q2. Explain null hypothesis and p-value in terms of probability
  • Ans. 

    Null hypothesis is a statement that assumes no relationship or difference between variables. P-value is the probability of obtaining results as extreme as the observed data, assuming the null hypothesis is true.

    • Null hypothesis is a statement that assumes no effect or relationship between variables

    • P-value is the probability of obtaining results as extreme as the observed data, assuming the null hypothesis is true

    • Null hy...

  • Answered by AI
  • Q3. Explain Linear and Logistic Regression
  • Ans. 

    Linear regression is used for predicting continuous numerical values, while logistic regression is used for predicting binary categorical values.

    • Linear regression models the relationship between a dependent variable and one or more independent variables using a linear equation.

    • Logistic regression models the probability of a binary outcome using a logistic function.

    • Linear regression is used for tasks like predicting hou...

  • Answered by AI

Interview Preparation Tips

Topics to prepare for Tiger Analytics Machine Learning Engineer interview:
  • SQL
  • Machine Learning
  • Data Science
  • Python
Interview preparation tips for other job seekers - Prepare for data science related questions and basic machine learning algorithms (Decision tree, PCA, etc.). Also prepare for SQL queries.

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. Python question on list comprehension
  • Q2. Python related questions

Interview Questionnaire 

2 Questions

  • Q1. Resume Summarisation
  • Q2. Common Puzzeles

Interview Preparation Tips

Round: Test
Experience: It was a online test.
Tips: Time given was ample. Accuracy matteredin their test which was conducted by Huntshire.com. Do visit their site for sample questions
Duration: 60 minutes

Round: Group Discussion
Tips: 9 people were in a group. Time for GD was 20 minutes including 5 minutes for summarising.
Duration: 20 minutes

Round: Interview
Experience: Firstly, I summarized and explained the key highlights of my resume in about 5-6 minutes. Then, based on my resume they started asking some HR questions and tried to grill me on those. After that, theyasked me some common puzzles.
Tips: There was a single PI round. The panel consisted of 3 members. The HR and resume based questions that are generally asked in an interview are different for every person. So, it's in the hands of the candidate to turn the tide of questions in his favor by ending the answer to the last question on a positive note. The next  question that the interviewer asks will be based on the last answered question.You will get the common puzzles on googling. Just write " 25 horses puzzle" and " 100 doors puzzle". They also asked me to form numbers from 1to 15 using only four 4's and any mathematical operator.

Skill Tips: ""
College Name: IIT ROORKEE

AIMonk Interview FAQs

How many rounds are there in AIMonk interview?
AIMonk interview process usually has 2 rounds. The most common rounds in the AIMonk interview process are Technical, Assignment and Resume Shortlist.
How to prepare for AIMonk interview?
Go through your CV in detail and study all the technologies mentioned in your CV. Prepare at least two technologies or languages in depth if you are appearing for a technical interview at AIMonk. The most common topics and skills that interviewers at AIMonk expect are Artificial Intelligence, Computer Vision, Javascript, MongoDB and Python.
What are the top questions asked in AIMonk interview?

Some of the top questions asked at the AIMonk interview -

  1. what is accuracy in...read more
  2. what is recall in...read more
  3. Decorators in pyt...read more

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AIMonk Interview Process

based on 4 interviews

Interview experience

4.3
  
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AIMonk Reviews and Ratings

based on 6 reviews

4.3/5

Rating in categories

4.2

Skill development

4.0

Work-life balance

3.7

Salary

3.9

Job security

3.7

Company culture

3.7

Promotions

4.0

Work satisfaction

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