MResult Services Technology Analyst Interview Questions and Answers

Q1. Technical round totally based on client. Client is taking interview. Technical lead has taken interview all questions related to your skill sets.
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Q2. Front-end ReactJs 1. Hooks (useEffect, useState) 2. how to increase performance of your app. 3. virtual-Dom 4. functional components Javascripts & Node: 1.SetTimeout, setImmediate given script which one w...read more
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Q1. Technical manager round 1. Introduction & project explain 2. Performance improvement technique front-end & backend 3. Logical coding question need to solve
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Q1. Salary discussion, location negotiations. They will say 3 years client will fixed but reality is different.
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Q2. Salary negotiation very quick decision.
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Q1. Windows technology
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Q1. Scenario based questions
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Q1. Salary discusssion
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Q1. They first ask me about my project then ask more how it was working ,and more deep questions on that to understand how much I know about the project I worked on .
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Q2. Spring Authentitation and Authorization,And Kubernetes
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3 SQL questions of basic, intermediate and advance level respectively.

Q1. Technical Questions related to past work
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Q1. Salary negotiation and offer.
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Asked to write Selenium code to launch flipkart and handle the popup that shows up and asked to write code to reverse an entire string later asked reverse each word in that string without reversing the relative order of that string

It was a 30 minutes round questions on Python, SQL, Pyspark - scenario based question to write the code.

Q1. Here you will be asked few theoretical question like defination, where to use, what is the advantage etc. - Focused on Azure (ADF,ADLS,Synapse), SQL, Pyspark,python
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Q2. SQL Optimization
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Q3. Dynamic file ingestion in ADF
Ans. 
Dynamic file ingestion in ADF involves using parameters to dynamically load files into Azure Data Factory.
• Use parameters to specify the file path and name dynamically

• Utilize expressions to dynamically generate file paths

• Implement dynamic mapping data flows to handle different file structures
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Q4. Agregation in databricks
Ans. 
Aggregation in Databricks refers to the process of combining and summarizing data in a Spark environment.
• Aggregation functions like sum, count, avg, min, max can be used in Databricks to summarize data.

• Grouping data based on certain columns and then applying aggregation functions is a common practice.

• Aggregation can be performed on large datasets efficiently using Databricks' distributed computing capabilities.
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Q1. It would be basic HR round to understand your expectation and expertise.
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Screen sharing with code testing

Intro and some code test

.net and details information of pervious company

Q1. Salary discussion
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