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Exam Professional Data Engineer All Questions

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Exam Professional Data Engineer topic 1 question 30 discussion

Actual exam question from Google's Professional Data Engineer
Question #: 30
Topic #: 1
[All Professional Data Engineer Questions]

Your company's customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations.
The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?

  • A. Add a node to the MySQL cluster and build an OLAP cube there.
  • B. Use an ETL tool to load the data from MySQL into Google BigQuery.
  • C. Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.
  • D. Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️
by jvg637 at March 15, 2020, 12:56 p.m.

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HectorLeon2099
Highly Voted 3 years, 11 months ago
It is a GOOGLE exam. The answer won't be on-premise or OLAP cubes even if it is the easiest. The answer is B
upvoted 113 times
Tanzu
2 years, 10 months ago
choose dataproc over hadoop cluster chose bigquery over all.. there is no special customer requirement that gonna drive us to hadoop or dataproc.
upvoted 9 times
cetanx
1 year, 10 months ago
Answer - B mysql dump: This utility creates a logical backup and a flat file containing the SQL statements that can be run again to bring back the database to the state when this file was created. So this file can easily be processed by an ETL tool and loaded into BQ.
upvoted 2 times
ThorstenStaerk
1 year, 7 months ago
So, you are saying that B takes the backup data from the nightly dumps? How can you be sure?
upvoted 2 times
cetanx
1 year, 6 months ago
I agree that B sounds like running ETL directly on the database. It doesn't say anything explicitly about using dumps. However, by leveraging the Dataproc JDBC Connector, one can perform various operations such as querying, joining, filtering, and aggregating data from your SQL databases within your Dataproc jobs. This can be particularly useful when you want to combine data from multiple sources or perform complex data transformations before processing the data further. So with D, you can run your analysis from a separate cloud-sql instance created from the dump and without affecting the production database.
upvoted 1 times
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Preetmehta1234
9 months, 1 week ago
That’s so true! This should be the first logic for elimination
upvoted 1 times
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[Removed]
Highly Voted 4 years, 8 months ago
Answer: D Description: Easy and it won’t affect processing
upvoted 39 times
dambilwa
4 years, 5 months ago
Agreed- Option[D] is most appropriate in this scenario
upvoted 5 times
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StefanoG
3 years, 2 months ago
So I vote for B
upvoted 6 times
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Alexej_123
3 years, 10 months ago
I think it is B and not D: 1) There are no info regarding date freshness required for analytics. So nightly backup might be not enough as a source because it will only provide info one tie a day. 2) Dataproc is recommended as easiest way for migration of hadoop processes. SO no reason to use Dataproc for designing a new analytics processes. 3) The solution is really very limited if you will extend it in the future and add new data sources or create new aggregate tables. Where they should be created? 4) There is no info on which version is on prem MySQL database (I am not an expert in MySql) but I can imagine there might be compartibility issue for backup / restore between different releases
upvoted 15 times
StefanoG
3 years, 2 months ago
I also think the answer is D, because on B it is not written that the source is the backup but (directly) MYSQL. So wit this solution we add requests on MySQL and so, mpacting the operations-
upvoted 3 times
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g2000
3 years, 10 months ago
but how about the impact on operation? D seems better match.
upvoted 1 times
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hellofrnds
3 years, 1 month ago
" Dataproc makes open source data and analytics processing fast, easy, and more secure in the cloud ". Please refer this google link. https://cloud.google.com/blog/products/data-analytics/genomics-data-analytics-with-cloud-pt2
upvoted 1 times
sergio6
3 years ago
The link titles "Genomics analysis with Hail, BIGQUERY, and Data Proc", the solution describes the use of Bigquery to do analytics
upvoted 1 times
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StefanoG
3 years, 2 months ago
Google Cloud Dataproc is not an analytic tool
upvoted 10 times
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meh_33
Most Recent 3 months, 2 weeks ago
Believe me all questions were from Exam topic all were there yesterday in exam. But yes dont go with starting questions mainly focus questions after 200 and latest questions are at last page.
upvoted 1 times
Gayatri147
3 months, 1 week ago
How you accessed questions after question number 70 it is asking for pro subscription ?
upvoted 1 times
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mark1223jkh
6 months, 1 week ago
Answer B: I don't know why people are choosing D. It is two steps, first cloudsql and then dataproc, a lot of overhead. BigQuery is just perfect fit.
upvoted 1 times
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0725f1f
8 months, 3 weeks ago
Selected Answer: D
This won’t affect processing
upvoted 1 times
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TVH_Data_Engineer
1 year ago
Selected Answer: B
Based on these considerations, option B is likely the best approach. By using an ETL tool to load data from MySQL into Google BigQuery, you're leveraging BigQuery's strengths in handling large-scale analytics workloads without impacting the performance of the operational databases. This option provides a clear separation of operational and analytical workloads and takes advantage of BigQuery's fast analytics capabilities.
upvoted 2 times
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axantroff
1 year ago
Selected Answer: B
Do not spend much time on in - just B
upvoted 1 times
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rocky48
1 year ago
Selected Answer: B
Answer is B - Use an ETL tool to load the data from MySQL into Google BigQuery. * Google BigQuery is a serverless, highly scalable data warehouse that can handle large-scale analytics workloads without impacting your MySQL cluster's performance. * Using an ETL (Extract, Transform, Load) tool to transfer data from MySQL to BigQuery allows you to maintain a separate analytics environment, ensuring that your operational database remains unaffected. Option C (connecting an on-premises Apache Hadoop cluster to MySQL and performing ETL) introduces complexity and may not be as scalable as a cloud-based solution. Option D (mounting backups to Google Cloud SQL and processing the data using Google Cloud Dataproc) could be an option for historical data analysis but might not be the best choice for real-time analytics while the MySQL cluster is under heavy load. Additionally, the backups need to be restored and processed, which might introduce some delay.
upvoted 2 times
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mk_choudhary
1 year, 1 month ago
It's GOOGLE exam where choosing the GCP service shall be first preference. Now notice the problem statement "perform analytics with minimal impact on operations" BigQuery is right option for analytic as well as Cloud SQL does provide easy export to GCS where we can query from BigQuery without loading into BQ to save storage cost.
upvoted 2 times
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rtcpost
1 year, 1 month ago
Selected Answer: B
B. Use an ETL tool to load the data from MySQL into Google BigQuery. * Google BigQuery is a serverless, highly scalable data warehouse that can handle large-scale analytics workloads without impacting your MySQL cluster's performance. * Using an ETL (Extract, Transform, Load) tool to transfer data from MySQL to BigQuery allows you to maintain a separate analytics environment, ensuring that your operational database remains unaffected. Option C (connecting an on-premises Apache Hadoop cluster to MySQL and performing ETL) introduces complexity and may not be as scalable as a cloud-based solution. Option D (mounting backups to Google Cloud SQL and processing the data using Google Cloud Dataproc) could be an option for historical data analysis but might not be the best choice for real-time analytics while the MySQL cluster is under heavy load. Additionally, the backups need to be restored and processed, which might introduce some delay.
upvoted 3 times
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melligeri
1 year, 1 month ago
Selected Answer: B
The question clearly says there is load on MYSQL already so doing analytics on it is bad idea. Its bad to run analytics on MYSQL but still a better option to run etl with it to load it to BigQuery.
upvoted 1 times
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imran79
1 year, 1 month ago
B. Use an ETL tool to load the data from MySQL into Google BigQuery. This way, analytics is entirely separated from the operational database, and BigQuery is well-suited for large-scale analytics.
upvoted 2 times
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emmylou
1 year, 1 month ago
The correct answer is to build a read replica :-) but since we can't do that then migrating to BigQuery will have to suffice.
upvoted 2 times
Fotofilico
1 year, 1 month ago
thanks! :3
upvoted 1 times
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Nirca
1 year, 2 months ago
Selected Answer: D
Answer is Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc. A: OLAP on MySQL performs poorly. B: ETL consumes lot of MySQL resources, to read the data, as per question MySQL is under pressure already. C: Similar to B. D: By mounting backup can avoid reading from MySQL, data freshness is not an issue as per the question (and is not mention in the question).
upvoted 1 times
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emmylou
1 year, 2 months ago
Wouldn't the correct answer be to create read replica and do analytics off of that?
upvoted 1 times
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boraxer1
1 year, 2 months ago
Selected Answer: D
Answer is Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc. A: OLAP on MySQL performs poorly. B: ETL consumes lot of MySQL resources, to read the data, as per question MySQL is under pressure already. C: Similar to B. D: By mounting backup can avoid reading from MySQL, data freshness is not an issue as per the question (and is not mention in the question). Reference: https://cloud.google.com/blog/products/data-analytics/genomics-data-analytics-with-cloud-pt2
upvoted 1 times
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FP77
1 year, 4 months ago
Selected Answer: B
Why overcomplicate things by using Dataproc? I choose B
upvoted 3 times
FP77
1 year, 4 months ago
I meant hadoop cluster
upvoted 1 times
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