Accenture Analytics Analyst Interview Questions and Answers

Q1. Project related questions from your CV
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Q1. Question on transformers
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Q2. Comparison of transfer learning and fintuning.
Ans. 
Transfer learning involves using pre-trained models on a different task, while fine-tuning involves further training a pre-trained model on a specific task.
• Transfer learning uses knowledge gained from one task to improve learning on a different task.

• Fine-tuning involves adjusting the parameters of a pre-trained model to better fit a specific task.

• Transfer learning is faster and requires less data compared to training a...read more
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*****, arjumpudi satyanarayana

Q1. What is the python language
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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. Tell me about yourself?
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I am a data scientist with a background in statistics and machine learning, passionate about solving complex problems using data-driven approaches.
• Background in statistics and machine learning

• Experience in solving complex problems using data-driven approaches

• Passionate about leveraging data to drive insights and decision-making
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Q2. Describe in detail about one of my main project.
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Developed a predictive model for customer churn in a telecom company.
• Collected and cleaned customer data including usage patterns and demographics.

• Used machine learning algorithms such as logistic regression and random forest to build the model.

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

• Implemented the model into the company's CRM system for real-time predictions.
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Q3. Few questions related to my projects.
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Q1. Questions on Basics python(Since i am fresher)
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The first round was easy which consist of basic aptitude questions including java,c,cpp,dbms,sql,reasoning,etc

Q1. Explain Data Analysis Life Cycle
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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?
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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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Q1. Asked questions related to stats , Senario based. Interviewer was very nice
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Q2. Asked questions on Python Programming and asked for Programs Like How can update List , Asked to Build a Tough Dict , Class etc.
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Q1. Why u want to join?
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Q2. What is your future goal?
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