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Avasoft Written Test Preparation

The document is a preparation pack for Avasoft's written test, divided into three sections: Python programming practice with 15 problems, SQL practice questions with 10 problems, and a cheat sheet on AI/ML theory. The Python section includes tasks like checking even/odd numbers, calculating factorials, and string manipulations, while the SQL section covers queries related to employee data. The AI/ML section provides definitions and concepts such as supervised learning, overfitting, and common Python libraries used in data science.

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3K views2 pages

Avasoft Written Test Preparation

The document is a preparation pack for Avasoft's written test, divided into three sections: Python programming practice with 15 problems, SQL practice questions with 10 problems, and a cheat sheet on AI/ML theory. The Python section includes tasks like checking even/odd numbers, calculating factorials, and string manipulations, while the SQL section covers queries related to employee data. The AI/ML section provides definitions and concepts such as supervised learning, overfitting, and common Python libraries used in data science.

Uploaded by

nicholasthomas0105
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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Avasoft Written Test Preparation Pack

Section 1: Python Programming Practice (15 Problems)


 1. Write a Python program to check if a number is even or odd.
 2. Write a Python function to return the factorial of a number.
 3. Write a program to find the largest of three numbers.
 4. Reverse a string without using any built-in reverse functions.
 5. Write a program to count vowels in a given string.
 6. Write a function to check if a string is a palindrome.
 7. Find the sum of all elements in a list.
 8. Find the maximum and minimum element in a list.
 9. Write a program to print Fibonacci series up to n terms.
 10. Create a dictionary and print all its keys and values.
 11. Count the frequency of each character in a string using a dictionary.
 12. Find whether an element exists in a list or not.
 13. Use list comprehension to create a list of squares of even numbers from 1 to 10.
 14. Write a program to remove duplicates from a list.
 15. Write a Python function to find the GCD of two numbers.

Section 2: SQL Practice Questions (10 Problems)


 1. Write a SQL query to select all records from a table called Employees.
 2. Select the name and salary of employees whose salary is greater than 50000.
 3. Write a query to count the number of employees in each department.
 4. Write a query to retrieve all employees who joined between 2020 and 2023.
 5. Write a query to find the average salary in the company.
 6. Get all employee names who have 'Manager' in their job title.
 7. Write a query to display department-wise total salary spent.
 8. Perform an INNER JOIN between Employees and Departments on department_id.
 9. Write a query to fetch top 5 highest paid employees.
 10. Write a query to find employees whose department is not in (Sales, Marketing).

Section 3: AI/ML Theory Cheat Sheet


**AI (Artificial Intelligence)**: Simulation of human intelligence in machines.

**Machine Learning (ML)**: A subset of AI where machines learn from data.

**Supervised Learning**: Model is trained on labeled data (e.g., classification, regression).

**Unsupervised Learning**: Model finds hidden patterns in unlabeled data (e.g., clustering).
**Classification**: Predicting a category (e.g., spam or not spam).

**Regression**: Predicting a continuous value (e.g., price of a house).

**Overfitting**: Model performs well on training data but poorly on unseen data.

**Underfitting**: Model is too simple and performs poorly on both training and test data.

**Confusion Matrix**: Table used to evaluate the performance of a classification algorithm.

**Accuracy**: (TP + TN) / Total predictions.

**Precision**: TP / (TP + FP) – good for low false positives.

**Recall**: TP / (TP + FN) – good for low false negatives.

**Common Python Libraries**: pandas, numpy, matplotlib, scikit-learn.

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