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Cloud Analogy Ai Ml Engineer Interview Questions and Answers

Updated 20 Nov 2024

Cloud Analogy Ai Ml Engineer Interview Experiences

1 interview found

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

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

Round 1 - One-on-one 

(2 Questions)

  • Q1. Introduction OF Machine Learning
  • Ans. 

    Machine learning is a subset of artificial intelligence that focuses on developing algorithms and models that can learn from and make predictions or decisions based on data.

    • Machine learning involves training algorithms to learn patterns and make predictions from data.

    • It can be supervised, unsupervised, or semi-supervised learning.

    • Examples include image recognition, natural language processing, and recommendation system

  • Answered by AI
  • Q2. Last year project
  • Ans. 

    Developed a machine learning model to predict customer churn for a telecom company.

    • Used historical customer data to train the model

    • Implemented various classification algorithms such as Random Forest and Logistic Regression

    • Evaluated model performance using metrics like accuracy, precision, and recall

  • Answered by AI
Round 2 - One-on-one 

(1 Question)

  • Q1. Basics of Machine Learning

Interview Preparation Tips

Interview preparation tips for other job seekers - It was a nice Experience but interview was mainly focus on greater understanding of the concepts

Skills evaluated in this interview

Interview questions from similar companies

Interview experience
4
Good
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
No response

I applied via Naukri.com and was interviewed in Dec 2024. There was 1 interview round.

Round 1 - Technical 

(1 Question)

  • Q1. Difference between Lemmatization and stemming
  • Ans. 

    Lemmatization produces the base or dictionary form of a word, while stemming reduces words to their root form.

    • Lemmatization considers the context and meaning of the word, resulting in a valid word that makes sense.

    • Stemming simply chops off prefixes or suffixes, potentially resulting in non-existent words.

    • Example: Lemmatization of 'better' would result in 'good', while stemming would reduce it to 'bet'.

  • Answered by AI
Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. What are types of prompts?
  • Ans. 

    Types of prompts include text prompts, image prompts, audio prompts, and video prompts.

    • Text prompts: prompts that are in written form

    • Image prompts: prompts that are in visual form

    • Audio prompts: prompts that are in audio form

    • Video prompts: prompts that are in video form

  • Answered by AI
  • Q2. What is the difference between the Symmetric and Asymmetric?
  • Ans. 

    Symmetric encryption uses the same key for both encryption and decryption, while asymmetric encryption uses different keys for encryption and decryption.

    • Symmetric encryption is faster and more efficient than asymmetric encryption.

    • Asymmetric encryption provides better security as it uses a public key for encryption and a private key for decryption.

    • Examples of symmetric encryption algorithms include AES and DES, while ex...

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Keep focus on your project explanation

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Referral and was interviewed in Jul 2024. There were 3 interview rounds.

Round 1 - Assignment 

30 MCQs where 15 Need to be answered correctly to get shortlisted.
Sanfoundary source is very helpful in cracking it.

Round 2 - Technical 

(4 Questions)

  • Q1. Explain Oops concepts
  • Ans. 

    Oops concepts refer to Object-Oriented Programming principles such as Inheritance, Encapsulation, Polymorphism, and Abstraction.

    • Inheritance: Allows a class to inherit properties and behavior from another class.

    • Encapsulation: Bundling data and methods that operate on the data into a single unit.

    • Polymorphism: Ability to present the same interface for different data types.

    • Abstraction: Hiding the complex implementation det

  • Answered by AI
  • Q2. Explain file handling
  • Ans. 

    File handling refers to the process of managing and manipulating files on a computer system.

    • File handling involves tasks such as creating, reading, writing, updating, and deleting files.

    • Common file operations include opening a file, reading its contents, writing data to it, and closing the file.

    • File handling in programming languages often involves using functions or libraries specifically designed for file operations.

    • E...

  • Answered by AI
  • Q3. Explain supervised and unsupervised learning algorithms of your choice.
  • Ans. 

    Supervised learning uses labeled data to train a model, while unsupervised learning finds patterns in unlabeled data.

    • Supervised learning requires input-output pairs for training

    • Examples include linear regression, support vector machines, and neural networks

    • Unsupervised learning clusters data based on similarities or patterns

    • Examples include k-means clustering, hierarchical clustering, and principal component analysis

  • Answered by AI
  • Q4. Coding question on pandas which had 10 followup questions
Round 3 - HR 

(1 Question)

  • Q1. Simple discussion on compensation

Interview Preparation Tips

Interview preparation tips for other job seekers - Easy to moderate interview. Stay focused on basics.

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Naukri.com and was interviewed in Jul 2024. There was 1 interview round.

Round 1 - Technical 

(5 Questions)

  • Q1. Describe any NLP project end to end.
  • Ans. 

    Developed a sentiment analysis model using NLP to analyze customer reviews for a product.

    • Collected and preprocessed text data from various sources

    • Performed tokenization, stopword removal, and lemmatization

    • Built a machine learning model using techniques like TF-IDF and LSTM

    • Evaluated the model's performance using metrics like accuracy and F1 score

    • Deployed the model for real-time sentiment analysis of new reviews

  • Answered by AI
  • Q2. What is cosine similarity?
  • Ans. 

    Cosine similarity is a measure of similarity between two non-zero vectors in an inner product space.

    • It measures the cosine of the angle between the two vectors.

    • Values range from -1 (completely opposite) to 1 (exactly the same).

    • Used in recommendation systems, text mining, and clustering algorithms.

  • Answered by AI
  • Q3. What is difference between iterator and iterable?
  • Ans. 

    Iterator is an object that allows iteration over a collection, while iterable is an object that can be iterated over.

    • Iterator is an object with a next() method that returns the next item in the collection.

    • Iterable is an object that has an __iter__() method which returns an iterator.

    • Example: List is iterable, while iter(list) returns an iterator.

  • Answered by AI
  • Q4. Write a python function for cosine similarity.
  • Ans. 

    Python function to calculate cosine similarity between two vectors.

    • Define a function that takes two vectors as input.

    • Calculate the dot product of the two vectors.

    • Calculate the magnitude of each vector and multiply them.

    • Divide the dot product by the product of magnitudes to get cosine similarity.

  • Answered by AI
  • Q5. How did you evaluate your model. what is F1 score.
  • Ans. 

    F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall.

    • F1 score is the harmonic mean of precision and recall, calculated as 2 * (precision * recall) / (precision + recall).

    • It is a better metric than accuracy when dealing with imbalanced datasets.

    • A high F1 score indicates a model with both high precision and high recall.

    • F1 score ranges from 0 to 1, where

  • Answered by AI

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Company Website and was interviewed in Jul 2023. There were 4 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 Resume tips
Round 2 - HR 

(1 Question)

  • Q1. Normal HR round that happens as the initial screening process
Round 3 - Coding Test 

Python basic coding questions (pandas, numpy, OOPs concept.
AI ML theory questions

Round 4 - HR 

(1 Question)

  • Q1. Normal salary discussion
Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
Not Selected
Round 1 - Group Discussion 

Discussion on Gradient, SGD, K Mean ++, Silhouette Score, How to Handle High Variation Data,
Coding asked to code KNN, Hyper-Parameter Tuning, Two Difficult Questions on Coding...DSA Based Stumped on Those.

Verdict... Not Selected

Round 2 - Coding Test 

Simple Coding No Chat GPT Support Should Be There

Interview Preparation Tips

Interview preparation tips for other job seekers - Python DSA + Some Sort of Online TCS YouTube Videos.
Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - One-on-one 

(2 Questions)

  • Q1. Ask me on project
  • Q2. Aws, sql, python, flask ask me

Interview Preparation Tips

Interview preparation tips for other job seekers - Not selected
Interview experience
3
Average
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Naukri.com and was interviewed in Jan 2024. There was 1 interview round.

Round 1 - Technical 

(1 Question)

  • Q1. Q. explain your project Q. Project-related questions asked basic to hard Q. What is backpropagation in deep learning? Q. What is the Hugging Face module and working Q. Object detection YOLO NAS Vs YOLO 8

Interview Preparation Tips

Interview preparation tips for other job seekers - interview medium not much hard
Interview experience
3
Average
Difficulty level
Moderate
Process Duration
-
Result
No response

I was interviewed in Oct 2024.

Round 1 - Technical 

(3 Questions)

  • Q1. What is OHE ( one hot encoding)
  • Ans. 

    OHE is a technique used in machine learning to convert categorical data into a binary format.

    • OHE is used to convert categorical variables into a format that can be provided to ML algorithms.

    • Each category is represented by a binary vector where only one element is 'hot' (1) and the rest are 'cold' (0).

    • For example, if we have a 'color' feature with categories 'red', 'blue', 'green', OHE would represent them as [1, 0, 0],

  • Answered by AI
  • Q2. What is conditional probability
  • Ans. 

    Conditional probability is the likelihood of an event occurring given that another event has already occurred.

    • Conditional probability is calculated using the formula P(A|B) = P(A and B) / P(B)

    • It represents the probability of event A happening, given that event B has already occurred

    • Conditional probability is used in various fields such as machine learning, statistics, and finance

  • Answered by AI
  • Q3. What is precision, recall
  • Ans. 

    Precision and recall are evaluation metrics used in machine learning to measure the performance of a classification model.

    • Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

    • Recall is the ratio of correctly predicted positive observations to the all observations in actual class.

    • Precision is important when the cost of false positives is high, while recall is i...

  • Answered by AI

Skills evaluated in this interview

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Cloud Analogy Interview FAQs

How many rounds are there in Cloud Analogy Ai Ml Engineer interview?
Cloud Analogy interview process usually has 2 rounds. The most common rounds in the Cloud Analogy interview process are One-on-one Round.
What are the top questions asked in Cloud Analogy Ai Ml Engineer interview?

Some of the top questions asked at the Cloud Analogy Ai Ml Engineer interview -

  1. Introduction OF Machine Learn...read more
  2. Basics of Machine Learn...read more

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