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Vernacular.AI Data Associate Interview Questions and Answers

Updated 11 Jun 2022

Vernacular.AI Data Associate Interview Experiences

1 interview found

I applied via Referral and was interviewed before Jun 2021. There were 3 interview rounds.

Round 1 - Assignment 

It was easy assignment

Round 2 - One-on-one 

(4 Questions)

  • Q1. Why do you want to join skit.ai
  • Q2. What is data annotation
  • Ans. 

    Data annotation is the process of labeling or tagging data to make it understandable for machines.

    • It involves adding metadata to data sets

    • It helps in training machine learning models

    • Examples include labeling images with objects or sentiments in text

    • It can be done manually or with the help of tools

    • It is important for improving accuracy and efficiency of AI systems

  • Answered by AI
  • Q3. How did you come to know about skit.ai
  • Q4. Do you know about what is the role of ai or bots
  • Ans. 

    AI or bots are computer programs designed to perform tasks that typically require human intelligence.

    • AI and bots can be used for various purposes such as customer service, data analysis, and automation of repetitive tasks.

    • They can be trained to learn from data and improve their performance over time.

    • Examples of AI and bots include chatbots, virtual assistants, and recommendation systems.

    • AI and bots have the potential t...

  • Answered by AI
Round 3 - Behavioral 

(1 Question)

  • Q1. Where are you from ? tell me about yourself & give your background to me

Interview Preparation Tips

Topics to prepare for Vernacular.AI Data Associate interview:
  • Data annotation
  • voice annotation
  • how to bots work
Interview preparation tips for other job seekers - Just study all the things from skit.ai/product-overview

Skills evaluated in this interview

Interview questions from similar companies

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Naukri.com and was interviewed in Dec 2024. There were 2 interview rounds.

Round 1 - Technical 

(2 Questions)

  • Q1. On Project from resume
  • Q2. GenAI basics
Round 2 - Technical 

(2 Questions)

  • Q1. RAG, LLM, Azure OpenAI
  • Q2. Python questions

Interview Preparation Tips

Topics to prepare for Axtria Data Scientist interview:
  • genai
  • Python
  • Llm
Interview preparation tips for other job seekers - Good interview process, good interviewer
Interview experience
5
Excellent
Difficulty level
Hard
Process Duration
-
Result
Not Selected

I applied via AmbitionBox and was interviewed in Dec 2024. There were 2 interview rounds.

Round 1 - Aptitude Test 

Aptitude test for selection.

Round 2 - One-on-one 

(2 Questions)

  • Q1. What is your understanding of data analytics?
  • Q2. What is the significance of data analytics in analyzing and visualizing data?

Interview Preparation Tips

Interview preparation tips for other job seekers - Always guide to the right place and make informed decisions based on good data analysis.
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Naukri.com and was interviewed in Dec 2024. There were 2 interview rounds.

Round 1 - Aptitude Test 

It was well designed

Round 2 - Technical 

(2 Questions)

  • Q1. Basic Questions about data warehousing
  • Q2. Dbt Scenarios

Interview Preparation Tips

Interview preparation tips for other job seekers - Prepare well for dbt and other ETL tools
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Approached by Company and was interviewed in Nov 2024. There were 3 interview rounds.

Round 1 - HR 

(1 Question)

  • Q1. Basicn details to check for qualifications
Round 2 - Technical 

(1 Question)

  • Q1. About my projects
Round 3 - Technical 

(1 Question)

  • Q1. More details about ML models
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
4-6 weeks
Result
Selected Selected

I applied via Company Website and was interviewed in Jul 2024. There were 5 interview rounds.

Round 1 - One-on-one 

(1 Question)

  • Q1. Basic ml related questions,past project discussion
Round 2 - One-on-one 

(1 Question)

  • Q1. AI, ML related, statistical analysis of the graph.
Round 3 - Behavioral 

(1 Question)

  • Q1. Work culture, check
Round 4 - cross functional lob 

(1 Question)

  • Q1. Work culture, project discussion, scenario based questions
Round 5 - HR 

(1 Question)

  • Q1. Basic HR questions, why sap salary discussions
Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - One-on-one 

(2 Questions)

  • Q1. Cache vs persist
  • Ans. 

    Cache is used for temporary storage of data in memory, while persist is used for saving data to disk for long-term storage.

    • Cache is typically faster as it stores data in memory for quick access.

    • Persist saves data to disk for durability and long-term storage.

    • Cache is often used for temporary data that can be recomputed if lost, while persist is used for important data that needs to be retained.

    • Examples: Using cache for ...

  • Answered by AI
  • Q2. Broadcast join and other optimization techniques
Round 2 - One-on-one 

(4 Questions)

  • Q1. Sql window function
  • Q2. Spark architecture, dataframe vs rdd
  • Q3. Python reverse sentence
  • Ans. 

    Reverse a sentence using Python

    • Split the sentence into words using split() method

    • Reverse the list of words using list slicing

    • Join the reversed list of words back into a sentence using join() method

  • Answered by AI
  • Q4. Join based on nulls in sql

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
No response

I applied via Approached by Company and was interviewed in Apr 2024. There was 1 interview round.

Round 1 - Technical 

(7 Questions)

  • Q1. I am a data entry operator
  • Q2. I am a Data entry operator .
  • Ans. I am a data entry operator.
  • Answered by Mohammad Ali Khan
  • Q3. I am Excel entry operator
  • Q4. Hindi typing speed 16
  • Q5. English typing speed 20
  • Q6. Fesser no experience
  • Q7. Data entry operator

Interview Preparation Tips

Interview preparation tips for other job seekers - I am Data Entry operator.i am 3 month experience. Hindi typing speed 15. English typing speed 20.
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
No response

I applied via Referral and was interviewed in Mar 2024. There were 2 interview rounds.

Round 1 - Technical 

(1 Question)

  • Q1. Basic of python , sql, pandas,niumpy
Round 2 - Technical 

(1 Question)

  • Q1. Gave me a task to make web app for medical condition detection after clearing first round asked questions relted to it
Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Group Discussion 

Highly interactive and technical

Round 2 - Aptitude Test 

1 hr test with focus on math and analytical thinking

Vernacular.AI Interview FAQs

How many rounds are there in Vernacular.AI Data Associate interview?
Vernacular.AI interview process usually has 3 rounds. The most common rounds in the Vernacular.AI interview process are Assignment, One-on-one Round and Behavioral.
What are the top questions asked in Vernacular.AI Data Associate interview?

Some of the top questions asked at the Vernacular.AI Data Associate interview -

  1. do you know about what is the role of ai or b...read more
  2. what is data annotat...read more

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based on 1 Vernacular.AI interview
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Vernacular.AI Data Associate Salary
based on 9 salaries
₹2 L/yr - ₹3 L/yr
35% less than the average Data Associate Salary in India
View more details

Vernacular.AI Data Associate Reviews and Ratings

based on 1 review

2.0/5

Rating in categories

2.0

Skill development

2.0

Work-Life balance

1.0

Salary & Benefits

4.0

Job Security

2.0

Company culture

1.0

Promotions/Appraisal

3.0

Work Satisfaction

Explore 1 Review and Rating
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