Quantiphi Analytics Solutions Private Limited Interview Questions, Process, and Tips

Basic Hackerrank test

Q1. Mostly project discussion
Add your answer

Basic apti,qunats,logical,english,ml,code

3question easy to medium

Q1. Stack que ,push pop operations
Add your answer
Q2. Project que,AI discussion
Add your answer

Q1. Describe a situation where you have worked with a colleague who always disagrees with you.
Add your answer
Q2. Describe a problem where you learned from failure
Ans. 
Failed to implement a new project management tool due to lack of user training
• Implemented a new project management tool without providing sufficient user training

• Users struggled to adapt to the new tool and productivity decreased

• Realized the importance of user training and communication in successful project implementations
Answered by AI
Add your answer
Q3. Behavioral questions
Add your answer

Case study was given to be solved

Q1. How do you present insights to higher management?
Ans. 
I present insights to higher management through clear and concise reports, visualizations, and presentations.
• Create visually appealing charts and graphs to highlight key insights

• Summarize findings in a clear and concise manner

• Use storytelling techniques to make the data relatable and impactful

• Provide recommendations based on the insights gathered

• Tailor the presentation to the audience's level of understanding and inter
Answered by AI
Add your answer
Q2. Situational questions
Add your answer

Q1. Salary negotiation
Add your answer

Q1. Different scores for model evaluations, embedding models
Ans. 
Different scores like accuracy, precision, recall, F1 for evaluating embedding models
• Common evaluation metrics for embedding models include accuracy, precision, recall, and F1 score

• Accuracy measures overall correctness of the model's predictions

• Precision measures the proportion of true positive predictions among all positive predictions

• Recall measures the proportion of true positive predictions among all actual positiv...read more
Answered by AI
Add your answer
Q2. How embedding models work
Ans. 
Embedding models learn to represent words or entities as dense vectors in a continuous vector space.
• Embedding models map words or entities to high-dimensional vectors where similar words have similar vectors.

• These models are trained using neural networks to learn the relationships between words based on their context.

• Popular embedding models include Word2Vec, GloVe, and FastText.

• Embedding models are commonly used in na...read more
Answered by AI
Add your answer
Q3. What is difference between precision and recall
Ans. 
Precision is the ratio of correctly predicted positive observations to the total predicted positive observations, while recall is the ratio of correctly predicted positive observations to the all observations in actual class.
• Precision focuses on the accuracy of positive predictions, while recall focuses on the proportion of actual positives that were correctly identified.

• Precision = TP / (TP + FP), Recall = TP / (TP + ...read more
Answered by AI
Add your answer

Q1. How word2vec works, how gensim works. what is tf-idf
Ans. 
word2vec is a technique to create word embeddings, gensim is a Python library for topic modeling and similarity detection, tf-idf is a method to represent the importance of a word in a document.
• word2vec is a neural network model that learns word embeddings by predicting the context of a word based on its surrounding words.

• Gensim is a Python library for topic modeling, document similarity analysis, and other natural lan...read more
Answered by AI
Add your answer

- Different Algorithm for sorting array

Q1. Basic design question
Add your answer
Q2. Based on the technology you are most familiar with react / java
Add your answer

Q1. BigQuery Architecture and file formats
Add your answer
Q2. Airflow operators and what is the use of Airflow python operator
Ans. 
Airflow operators are used to define tasks in a workflow. The Airflow Python operator is used to execute Python functions as tasks.
• Airflow operators are used to define individual tasks in a workflow

• The Airflow Python operator is specifically used to execute Python functions as tasks

• It allows for flexibility in defining custom tasks using Python code

• Example: PythonOperator(task_id='my_task', python_callable=my_python_fu
Answered by AI
Add your answer

Q1. Explain the entire ETL workload in your current company project and how to handle the data quality issues.
Add your answer