amazing

Thanks for the playlist. I scored 910 after referring to this playlist. Almost 90% of the questions were the same from this playlist only.🎉🎉

hi Priyanka, can you please upload the same content in PDF format please

The first statement should be "Yes", because of lazy evaluation. The action in the spark code line is "saveAsTable" which results in only reading the columns specified in the select function. Check: docs.databricks.com/en/pyspark/index.html#:~:text=Lazy%20evaluation%20means%20that%20DataFrames,model%20used%20by%20pandas%20DataFrames.

I am learning DP600 and that's what I need - questions and clear explainations Your videos are really really useful and helps me a lot Thanks a lot Priyanka

can you please upload Dp 203 videos

Thanks priyanka It helped me to clear the exam

Hi Priyanka! Thank you for MS Fabric relevant questions and answers. These questions and answers helped me in clearing my DP 600 certification. The way you explain correct answer with detail is commendable. Also why other options are incorrect are well explained by you. I am grateful to you.

Need pl-300

Thanks Priyanka. today, this palylist helped me to clear exam with score 955/1000 😊

The most likely cause of the failure is A. Query folding is occurring. Query folding is essential for incremental refresh to work efficiently. However, if the data source does not support query folding, or if there are issues with the query folding process, it can lead to excessive resource consumption and cause the refresh to fail1.

Can you provide me the full question pdf pls 😊

All the questions are addressed clearly, neatly, and concisely. Thank you so much !!

Thank you kindly for all the effort in the detailed explanations to the answers of these practice questions.

Priyanka can you share more questions in each video. Next Friday I have exam.

I have postponed any chance can you share this document?

Thank you 😊

Upload all questions once my exam in 3days..

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No questions today? 🤔

😍❤Welldone and thanks for these videos Priyanka!

Thank you so much your videos are really helpful can you please make playlist for DP-203 also

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Very Nice. Can you also share the difference between spark and databricks or how they are related

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can yu also make all questions at once instead of one after one please

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2 questions per day super helpful

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Super

Is it possible to post 2-3 question in a day?

Good work. Like the detailed explanations

Acronyms Spelled Out: ADF = Azure Data Factory ETL = Extract, Transform, Load GUI = Graphical User Interface (this was spelled out) API = Application Programming Interface

Thanks for the video but I’m a little confused. I thought that merge joins rely on equijoins. Your presentation shows the contrary. Could you please clarify?

HORRIBLE ACCENT🤮

Very well explained

How many did you answer correctly? 😃

This was a very good explanation. Thank you x

Hi, thanks for your tutorial... I am trying to apply what you said in my current task of my project and I'm having a dilemma. Please help me with this. I have 3 tables: table Title: - id - name table TitleRank: - id - name - title_id - rank table UserTitleRank -id -name -user_id I want to create FK at `UserTitleRank` to reference the `title_id` and `rank` at `TitleRank`. I have 2 choices: 1/ create composite FK `title_id` and `rank` at `UserTitleRank` table referencing `title_id` and `rank` of `TitleRank` 2/ create single FK `title_rank_id` referencing `id` of `TitleRank` Which one is better? What is the pros and cons of each and what is the implication and context I should take into account to select one over another? Thank you.

U were stucking fumbling alot while explaining. It is distracting Lister. So just try to improve that

youtube.com/@sweetvlogginggirl1190

Hi you used equi join in query but merge join will be applicable to non equi join can you calrify

very nice video. I have a small question @3:11 when its joining the employee with department on the hash key, how does it do it is it sequential? Many thanks

Your explanation is very helpful for me... Thanks and keep it up 😇