Data Science Internship In Chennai

Femtosoft Technologies Best DataScience Internship In Chennai is designed for students and aspiring data scientists looking to gain real-world experience in data analysis, machine learning, and AI applications. This internship provides hands-on exposure to working with large datasets, building predictive models, and applying statistical techniques to solve business problems.

Note!! After timer finished countdowning, quiz will be submitted automatically.

📝 Online Exam Instructions – Read Carefully Before Starting

1️⃣ Exam Details:

✅ Total Questions: (25 Questions)
✅ Exam Duration: (10 Minutes)
✅ Passing Criteria: (80%)

✅ Exam Type: MCQs

2️⃣ System Requirements:

✔ Use a laptop/desktop with a stable internet connection.
✔ Recommended browsers: Chrome, Firefox, Edge (latest versions).
✔ Ensure your webcam & microphone are working (if required).

3️⃣ Before Starting the Exam:

🖊 Fill in the Exam Registration Form Correctly

- - Full Name (This will be used for the certificate)
  - WhatsApp Number (Certificate will be sent to this number)
  - Email ID (Ensure it's correct; certificate & results will be emailed)

⚠️ Incorrect details may result in delays or disqualification.

4️⃣ Submission & Certification Process:

📌 Ensure all answers are submitted before the timer ends.
📌 Once submitted, you cannot change your responses.
📌 After passing, your certificate will be automatically generated with your registered name.

🎉 Congratulations on your success!

🎯 Stay focused and give your best! Good luck!

Please fill all the required fields carefully, information provided will be used in offer letter and Completion Certificate.

1 / 25

1. What is the purpose of activation functions in neural networks?

A. C) To initialize weights

B. B) To optimize the learning rate

C. A) To introduce non-linearity

D. D) None of the above

2 / 25

2. Explain the concept of clustering in unsupervised learning.

A. A) Grouping similar data points together

B. C) Only used for supervised tasks

C. D) None of the above

D. B) Dividing data into training and test sets

3 / 25

3. What is the purpose of feature scaling in machine learning?

A. A) To normalize the range of independent variables

B. B) To reduce dimensionality

C. D) None of the above

D. C) To transform categorical data

4 / 25

4. What is artificial intelligence (AI)?

A. D) None of the above

B. C) Only deals with machine learning

C. B) A method of feature engineering

D. A) Simulation of human intelligence processes by machines

5 / 25

5. What is cross-entropy loss?

A. D) None of the above

B. C) Used exclusively for clustering

C. B) Used for regression problems

D. A) A measure of how well a model's predicted probability aligns with actual labels

6 / 25

6. Describe what a loss function is.

A. B) A metric for accuracy

B. D) None of the above

C. A) A measure of how well a model performs

D. C) Only used in regression models

7 / 25

7. Explain the term "exploratory data analysis" (EDA).

A. A) Analyzing data sets to summarize their main characteristics

B. D) None of the above

C. B) A method for feature engineering

D. C) To generate regular reports

8 / 25

8. Why is data normalization important?

A. C) Both A and B

B. B) To decrease training times

C. A) To ensure uniformity and improve model performance

D. D) None of the above

9 / 25

9. What does the term "deep learning" refer to?

A. C) A method combined with decision trees

B. D) None of the above

C. B) Only a type of supervised learning

D. A) A subset of machine learning with neural networks

10 / 25

10. What is a generative adversarial network (GAN)?

A. B) A type of regression model

B. A) A framework where two neural networks contest with each other

C. C) Used only for classification tasks

D. D) None of the above

11 / 25

11. Describe the concept of collaborative filtering in recommendation systems.

A. B) Using content-based filtering

B. D) None of the above

C. A) Recommending items based on user interactions

D. C) Using decision trees to make suggestions

12 / 25

12. Describe the difference between L1 and L2 regularization.

A. C) Both A and B

B. D) None of the above

C. A) L1 shrinks coefficients to zero; L2 minimizes weights evenly

D. B) L1 minimizes weights linearly

13 / 25

13. Describe the difference between a parametric and non-parametric model.

A. C) Both A and B

B. B) Non-parametric does not rely on such assumptions

C. A) Parametric models assume a specific distribution

D. D) None of the above

14 / 25

14. What is the difference between bagging and boosting?

A. D) None of the above

B. C) Both A and B

C. B) Both methods improve accuracy

D. A) Bagging uses parallel models; boosting uses sequential

15 / 25

15. Explain what reinforcement learning is.

A. B) Teaching with labeled data

B. C) A supervised approach to training

C. A) Learning through trial and error

D. D) None of the above

16 / 25

16. Describe what transfer learning is.

A. A) Using a pre-trained model on a new but related task

B. B) Training a new model from scratch

C. D) None of the above

D. C) A synonym for supervised learning

17 / 25

17. What is the purpose of descriptive statistics?

A. A) To summarize and describe the main features of a dataset

B. C) To predict future outcomes

C. B) To analyze relationships between variables

D. D) None of the above

18 / 25

18. Explain the importance of feature engineering in machine learning.

A. A) It can considerably improve model performance

B. D) None of the above

C. B) It's optional

D. C) Only benefits deep learning models

19 / 25

19. What are some common challenges faced during model deployment?

A. D) None of the above

B. B) Data collection issues

C. A) Integration, scalability, and monitoring

D. C) Only trained models

20 / 25

20. What is an outlier, and why is it significant in data science?

A. A) A data point that deviates significantly from the rest

B. C) Represents missing data

C. D) None of the above

D. B) The average of a dataset

21 / 25

21. Explain the difference between fit, transform, and fit_transform methods in scikit-learn.

A. D) None of the above

B. C) All three are synonymous

C. B) fit_transform just trains the model

D. A) fit trains the model, transform applies it

22 / 25

22. What is the role of the loss function during training?

A. C) It has no role in training

B. B) To improve accuracy

C. D) None of the above

D. A) To measure how well the model's predictions match the actual outcomes

23 / 25

23. What is the purpose of one-hot encoding?

A. C) To normalize data

B. B) To reduce dimensionality

C. A) To convert categorical variables into binary format

D. D) None of the above

24 / 25

24. Explain what feature extraction is in the context of machine learning.

A. D) None of the above

B. B) It involves adding new features

C. A) The process of reducing the number of features

D. C) Only applicable to image data

25 / 25

25. What is the difference between primary and secondary features?

A. B) There is no difference

B. C) Primary features only exist in regression tasks

C. A) Primary features provide vital information; secondary features provide supplementary support

D. D) None of the above

Your score is

AI & DATASCIENCE INTENSHIP

Data Science Internship In Chennai

📝 Online Exam Instructions – Read Carefully Before Starting

1️⃣ Exam Details:

2️⃣ System Requirements:

3️⃣ Before Starting the Exam:

4️⃣ Submission & Certification Process: