Accenture Data Science Intern Interview Questions and Answers

Q1. What are Large Language Models?
Ans. 
Large Language Models are advanced AI models that can generate human-like text based on input data.
• Large Language Models use deep learning techniques to understand and generate text.

• Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).

• They are trained on vast amounts of text data to improve their language generation capabilities.
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Q2. Do you know about RAGs?
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RAGs stands for Red, Amber, Green. It is a project management tool used to visually indicate the status of tasks or projects.
• RAGs is commonly used in project management to quickly communicate the status of tasks or projects.

• Red typically indicates tasks or projects that are behind schedule or at risk.

• Amber signifies tasks or projects that are on track but may require attention.

• Green represents tasks or projects that ar...read more
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Q3. Which is the best clustering algorithm?
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There is no one-size-fits-all answer as the best clustering algorithm depends on the specific dataset and goals.
• The best clustering algorithm depends on the dataset characteristics such as size, dimensionality, and noise level.

• K-means is popular for its simplicity and efficiency, but may not perform well on non-linear data.

• DBSCAN is good for clusters of varying shapes and sizes, but may struggle with high-dimensional d...read more
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This was good aptitude test computer based

Coding round share screen and code

Q1. Explains OOPs concept
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Q2. Explain SOLID principles
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*****, arjumpudi satyanarayana

Q1. What is the python language
Ans. 
Python is a high-level programming language known for its simplicity and readability.
• Python is widely used for web development, data analysis, artificial intelligence, and scientific computing.

• It emphasizes code readability and uses indentation for block delimiters.

• Python has a large standard library and a vibrant community of developers.

• Example: print('Hello, World!')

• Example: import pandas as pd
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Q2. What is the code problems
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Code problems refer to issues or errors in the code that need to be identified and fixed.
• Code problems can include syntax errors, logical errors, or performance issues.

• Examples of code problems include missing semicolons, incorrect variable assignments, or inefficient algorithms.

• Identifying and resolving code problems is a key skill for data scientists to ensure accurate and efficient data analysis.
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Q3. What is the python code
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Python code is a programming language used for data analysis, machine learning, and scientific computing.
• Python code is written in a text editor or an integrated development environment (IDE)

• Python code is executed using a Python interpreter

• Python code can be used for data manipulation, visualization, and modeling
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Q4. What is the project
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The project is a machine learning model to predict customer churn for a telecommunications company.
• Developing predictive models using machine learning algorithms

• Analyzing customer data to identify patterns and trends

• Evaluating model performance and making recommendations for reducing customer churn
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Q5. What is the lnderssip
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The question seems to be incomplete or misspelled.
• It is possible that the interviewer made a mistake while asking the question.

• Ask for clarification or context to provide a relevant answer.
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Q1. Overfitting and Underfitting
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Q2. Find Nth-largest element
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Find Nth-largest element in an array
• Sort the array in descending order

• Return the element at index N-1
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Q3. NLP Data preprocessing
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Q1. Salary Discussion
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Q2. Fitment discussion
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Q1. Which GenAI projects I have worked on
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Q2. What is the context window in LLMs
Ans. 
Context window in LLMs refers to the number of surrounding words considered when predicting the next word in a sequence.
• Context window helps LLMs capture dependencies between words in a sentence.

• A larger context window allows the model to consider more context but may lead to increased computational complexity.

• For example, in a context window of 2, the model considers 2 words before and 2 words after the target word fo
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Q3. What is top_k parameter
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top_k parameter is used to specify the number of top elements to be returned in a result set.
• top_k parameter is commonly used in machine learning algorithms to limit the number of predictions or recommendations.

• For example, in recommendation systems, setting top_k=5 will return the top 5 recommended items for a user.

• In natural language processing tasks, top_k can be used to limit the number of possible next words in a
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Q4. What are regex patterns in python
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Regex patterns in Python are sequences of characters that define a search pattern.
• Regex patterns are used for pattern matching and searching in strings.

• They are created using the 're' module in Python.

• Examples of regex patterns include searching for email addresses, phone numbers, or specific words in a text.
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Q5. What are iterators and tuples
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Iterators are objects that allow iteration over a sequence of elements. Tuples are immutable sequences of elements.
• Iterators are used to loop through elements in a collection, like lists or dictionaries

• Tuples are similar to lists but are immutable, meaning their elements cannot be changed

• Example of iterator: for item in list: print(item)

• Example of tuple: my_tuple = (1, 2, 3)
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Q6. Do I have REST API experience
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Yes, I have experience working with REST APIs in various projects.
• Developed RESTful APIs using Python Flask framework

• Consumed REST APIs in data analysis projects using requests library

• Used Postman for testing and debugging REST APIs
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Forecasting problem - Predict daily sku level sales

Q1. What is difference between bias and variance
Ans. 
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
• Bias is the error introduced by approximating a real-world problem, leading to underfitting.

• Variance is the error introduced by modeling the noise in the training data, leading to overfitting.

• High bias can cause a model to miss relevant relationships between features and target variable.

• High variance can cause a model to ...read more
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Q2. Parametric vs non parametruc model
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Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
• Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.

• Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.

• Examples of parametric models inc...read more
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Q1. Explain XGBoost algoritm
Ans. 
XGBoost is a powerful machine learning algorithm known for its speed and performance in handling large datasets.
• XGBoost stands for eXtreme Gradient Boosting, which is an implementation of gradient boosting machines.

• It is widely used in machine learning competitions and is known for its speed and performance.

• XGBoost uses a technique called boosting, where multiple weak learners are combined to create a strong learner.

• It...read more
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Q2. XgBoost algorithm has 10-20 features. How are the splits decided, on which feature are they going to be divided?
Ans. 
XgBoost algorithm uses a greedy approach to determine splits based on feature importance.
• XgBoost algorithm calculates the information gain for each feature to determine the best split.

• The feature with the highest information gain is chosen for the split.

• This process is repeated recursively for each node in the tree.

• Features can be split based on numerical values or categories.

• Example: If a feature like 'age' has the hi...read more
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Q3. Do you have any experience on cloud platform?
Ans. 
Yes, I have experience working on cloud platforms such as AWS and Google Cloud.
• Experience with AWS services like S3, EC2, and Redshift

• Familiarity with Google Cloud services like BigQuery and Compute Engine

• Utilized cloud platforms for data storage, processing, and analysis
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Q4. What is entropy, information gain?
Ans. 
Entropy is a measure of randomness or uncertainty in a dataset, while information gain is the reduction in entropy after splitting a dataset based on a feature.
• Entropy is used in decision tree algorithms to determine the best feature to split on.

• Information gain measures the effectiveness of a feature in classifying the data.

• Higher information gain indicates that a feature is more useful for splitting the data.

• Entropy ...read more
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Q5. What is hypothesis testing?
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Hypothesis testing is a statistical method used to make inferences about a population based on sample data.
• Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

• The null hypothesis is assumed to be true until there is enough evidence to reject it.

• Statistical tests are used to determine the likelihood of observing the data if the null hypothesis is true.

• The p-value is used to determine ...read more
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Q6. Explain precision and recall, when are they used in which scenario?
Ans. 
Precision and recall are metrics used in evaluating the performance of classification models.
• Precision measures the accuracy of positive predictions, while recall measures the ability of the model to find all positive instances.

• Precision = TP / (TP + FP)

• Recall = TP / (TP + FN)

• Precision is important when false positives are costly, while recall is important when false negatives are costly.

• For example, in a spam email de...read more
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Q7. What is data imbalance?
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Data imbalance refers to unequal distribution of classes in a dataset, where one class has significantly more samples than others.
• Data imbalance can lead to biased models that favor the majority class.

• It can result in poor performance for minority classes, as the model may struggle to accurately predict them.

• Techniques like oversampling, undersampling, and using different evaluation metrics can help address data imbala...read more
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Q8. What is SMOTE? Do you have any experience working on Time Series? Code analysis of global variable?
Ans. 
SMOTE stands for Synthetic Minority Over-sampling Technique, used to balance imbalanced datasets by generating synthetic samples.
• SMOTE is commonly used in machine learning to address class imbalance by creating synthetic samples of the minority class.

• It works by generating new instances of the minority class by interpolating between existing instances.

• SMOTE is particularly useful in scenarios where the minority class i...read more
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Q9. Find 5th highest salary in every department. What are window functions Difference between union and union all Difference between delete and truncate.
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