BharatNXT Interview Questions and Answers

Optimizing the RAG Pipeline involves improving efficiency and accuracy of the pipeline for better performance.

• Optimize hyperparameters of the models used in the pipeline

• Implement feature engineering techniques to improve model performance

• Use efficient algorithms for processing data

• Parallelize tasks to reduce processing time

• Regularly monitor and update the pipeline for continuous improvement

Read a text file as one column in pyspark and get word counts

• Use SparkContext to read the text file and create a DataFrame with one column

• Split the text column into words using split() function

• Use groupBy() and count() functions to get the word counts

Chunking methods are used in natural language processing to group words into meaningful chunks.

• Regular expression based chunking: Uses regular expressions to define patterns for chunking.

• Rule-based chunking: Utilizes predefined rules to identify and group chunks.

• Statistical chunking: Involves using statistical models to automatically identify chunks.

• Shallow parsing: Identifies and groups chunks based on part-of-speech tags.

• Example: Chunking noun phrases in a sentence.

The code snippet is missing a closing bracket for the try block.

• The try block is missing a closing bracket which is causing a syntax error.

• Adding a closing bracket '}' after the catch block will resolve the issue.

I created various charts and KPIs to track project progress and performance.

• Created trend charts to visualize project progress over time

• Developed KPI dashboards to monitor key metrics such as cost savings and efficiency improvements

• Utilized pie charts to show distribution of resources or project outcomes

• Implemented bar charts to compare performance across different teams or departments

It depends on the situation. Quality is important for long-term success, while speed is crucial for meeting deadlines.

• Consider the project requirements and goals to determine whether quality or speed is more important.

• Prioritize quality when accuracy and precision are critical, such as in data analysis for decision-making.

• Choose speed when time-sensitive decisions need to be made quickly, such as in real-time analytics for operational efficiency.

• Strive to find a balance betwe...read more