Yazaki Data Analyst Interview Questions and Answers

Q1. It was general snowflake related
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Q2. Deep dive into architecture of snowflake
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Q1. Coding based round with onsite team members
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Q1. How you load the data using delta table in adf
Ans. 
You can load data using delta table in ADF by using the Copy Data activity and specifying the delta format.
• Use the Copy Data activity in ADF to load data into a delta table

• Specify the delta format in the sink settings of the Copy Data activity

• Ensure that the source data is compatible with the delta format
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Q1. Tricky sql questions
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Coding was fully based on python.
First they told me to on the laptop camera because it was a virtual interview and then they told me to share my screen
1. Starting from self introduction
2. Summarise my project in 2 minutes
3. Then he ask back to back 5-6 python.
questions and I have to write in vs
code ( on shared screen) ,I have to
write the code and then execute it.
They asked. few side questions like
from html and sql.
4. Total duration of interview is 30
minutes

Q1. These is based on communication, salary discussion, responsibilities and work culture.
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Q2. 1. He asked about cap between degree and job 2. Strength and can I work under pressure. 3. How can I manage if I assigned more than 1 project at a time
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Python coding + pandas + numpy+ reasoning

Q1. Discuss hypothesis testing in detail.
Ans. 
Hypothesis testing is a statistical method used to make inferences about a population based on sample data.
• Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

• The null hypothesis assumes that there is no significant difference or relationship between variables, while the alternative hypothesis suggests otherwise.

• A significance level (alpha) is chosen to determine the threshold for re...read more
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Q2. Hyper parameters of different DL algos.
Ans. 
Hyperparameters are settings that are external to the model and are used to control the learning process of deep learning algorithms.
• Hyperparameters for neural networks include learning rate, batch size, number of layers, number of neurons per layer, activation functions, and dropout rate.

• For example, in a convolutional neural network, hyperparameters may include filter size, stride, and padding.

• Hyperparameters can be ...read more
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