66degrees Interview Questions and Answers

I am a Cloud Engineer with a strong background in designing, implementing, and managing cloud infrastructure.

• Experienced in working with AWS, Azure, and Google Cloud Platform

• Skilled in automating tasks using tools like Terraform and Ansible

• Proficient in monitoring and optimizing cloud resources for cost efficiency

• Strong understanding of networking and security principles in cloud environments

Query to find employees with middle name 'Mohan' and salary over 2 lac.

• Use SELECT statement to retrieve data from employee table

• Use WHERE clause to filter employees with middle name 'Mohan' and salary over 2 lac

• Example: SELECT * FROM employee WHERE middle_name = 'Mohan' AND salary > 200000

A program to convert a string into uppercase and lowercase.

• Use built-in functions like toUpperCase() and toLowerCase() to convert the string.

• Iterate through each character of the string and convert it to uppercase or lowercase accordingly.

• Ensure to handle special characters and spaces appropriately.

Count common elements in two lists

• Iterate through both lists and count occurrences of each element

• Use a dictionary to store counts of elements in both lists

• Return the common elements and their counts

Developed a cloud-based data analytics platform using Python and AWS services

• Used AWS Lambda functions for serverless computing

• Implemented data processing pipelines with AWS Glue and S3

• Utilized Python libraries like Pandas and NumPy for data analysis

I have used various services in GCP including BigQuery, Dataflow, Cloud Storage, and Pub/Sub.

• BigQuery for data warehousing and analytics

• Dataflow for data processing and ETL

• Cloud Storage for storing and accessing data

• Pub/Sub for messaging and event-driven architectures

Data pipelines in GCP can be created using various tools like Dataflow, Dataproc, and Cloud Composer.

• Choose the appropriate tool based on the use case and data volume

• Define the data source and destination

• Create a pipeline using the chosen tool and define the data transformations

• Test and deploy the pipeline

• Monitor and troubleshoot the pipeline for any issues

Real-time data services can be used to migrate datawarehouse with GCP services.

• Use Cloud Dataflow to ingest and transform data in real-time

• Use Cloud Pub/Sub to stream data to BigQuery or Cloud Storage

• Use Cloud Dataproc to process data in real-time

• Use Cloud Composer to orchestrate data pipelines

• Use Cloud Spanner for real-time transactional data

• Use Cloud SQL for real-time relational data

• Use Cloud Bigtable for real-time N

Replication performance issues can arise due to network latency, hardware limitations, and inefficient queries.

• Network latency can cause delays in replication, leading to slower performance.

• Hardware limitations such as insufficient memory or slow disk speeds can also impact replication performance.

• Inefficient queries can cause replication to lag behind, leading to slower performance.

• Monitoring replication lag and optim...read more