Cloud Analogy Ai Ml Engineer Interview Questions and Answers

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

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
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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...read more
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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
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Q4. Coding question on pandas which had 10 followup questions
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Q1. Simple discussion on compensation
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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
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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.
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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.
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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.
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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
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Q1. Normal HR round that happens as the initial screening process
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Python basic coding questions (pandas, numpy, OOPs concept.
AI ML theory questions

Q1. Normal salary discussion
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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

Simple Coding No Chat GPT Support Should Be There

Q1. Ask me on project
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Q2. Aws, sql, python, flask ask me
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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
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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],
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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
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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...read more
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