Binary Semantics Associate Data Specialist Interview Questions and Answers

Q1. Python Lambda Function
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Q2. What are pods in Kubernetes
Ans. 
Pods are the smallest deployable units in Kubernetes, consisting of one or more containers.
• Pods are used to run and manage containers in Kubernetes

• Each pod has its own unique IP address within the Kubernetes cluster

• Pods can contain multiple containers that share resources and are scheduled together

• Pods are ephemeral and can be easily created, destroyed, or replicated

• Pods can be managed and scaled using Kubernetes contr
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Q1. How to create DBT project
Ans. 
To create a DBT project, you need to set up a project directory, create models, define sources, and run tests.
• Set up a project directory with a dbt_project.yml file

• Create models in the models directory using SQL files

• Define sources in the sources.yml file

• Run tests using dbt test command
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Q2. Explain materializations in dbt
Ans. 
Materializations in dbt are pre-computed tables that store the results of dbt models for faster query performance.
• Materializations are created using the 'materialized' parameter in dbt models.

• Common types of materializations include 'view', 'table', and 'incremental'.

• Materializations help improve query performance by reducing the need to recompute data on every query.

• Materializations can be refreshed manually or automa
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Q3. What are dbt snapshots?
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dbt snapshots are a way to capture the state of your data model at a specific point in time.
• dbt snapshots are used to create point-in-time snapshots of your data model

• They allow you to track changes in your data over time

• Snapshots can be used for auditing, debugging, or creating historical reports
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Coding test for all the student attending the first round, give your best practice every day all the best in your interview

Q1. What are your interest
Ans. 
My interests include data analysis, problem-solving, and continuous learning.
• Data analysis

• Problem-solving

• Continuous learning
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Q2. Java related question
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Verbal Apptitute , 30 min , 20 question

Q1. Self introduction
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Q1. My self and some tec question
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Q1. All tec questions
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Q1. Mostly Dax and sql related questions
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Q2. ETL related questions
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Q1. Project worked on
Ans. 
Developed a real-time data processing system for analyzing customer behavior
• Designed and implemented data pipelines using Apache Kafka and Spark

• Optimized data processing algorithms to handle large volumes of streaming data

• Collaborated with data scientists to integrate machine learning models into the system
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Q2. Snowflake questions on performance inhancement
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Q1. Basic ML Question, Timesereis and RNN with some questions on Comp Vision
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Q1. Project driven. Some coding
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Q1. Salary Discussion
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