10+ Accenture Ai Ml Engineer Interview Questions and Answers

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 examples of asymmetric encryption algorithms include RSA and...read more

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.

I would approach a machine learning problem by first understanding the problem, collecting and preprocessing data, selecting a suitable algorithm, training the model, evaluating its performance, and fine-tuning it.

• Understand the problem statement and define the objectives clearly.

• Collect and preprocess the data to make it suitable for training.

• Select a suitable machine learning algorithm based on the problem type (classification, regression, clustering, etc.).

• Train the model ...read more

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 1 is the best possible score.

I am currently working on developing machine learning models using Python, TensorFlow, and scikit-learn.

• Python programming language

• TensorFlow framework

• scikit-learn library

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

Developed a recommendation system for an e-commerce platform using collaborative filtering

• Used collaborative filtering to analyze user behavior and recommend products

• Implemented matrix factorization techniques to improve recommendation accuracy

• Evaluated model performance using metrics like RMSE and precision-recall curves

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.

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], [0, 1, 0], [0, 0, 1] respectively.

NLP (Natural Language Processing) in machine learning is the ability of a computer to understand, interpret, and generate human language.

• NLP enables machines to analyze and derive meaning from human language data.

• It involves tasks such as text classification, sentiment analysis, named entity recognition, and machine translation.

• Examples of NLP applications include chatbots, language translation services, and speech recognition systems.

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.

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

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

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

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 important when the cost of false negatives is high.

• F1 score...read more

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 details and showing only the necessary features.

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.

• Examples of file handling functions include fopen(), fread(...read more