Google Professional Data Engineer Exam - Topic 3 Question 100 Discussion

Actual exam question for Google's Professional Data Engineer exam

Question #: 100
Topic #: 3

[All Professional Data Engineer Questions]

You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

AUse Cloud SQL for storage. Add secondary indexes to support query patterns.

BUse Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.

CUse Cloud Spanner for storage. Add secondary indexes to support query patterns.

DUse Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

Show Suggested Answer

Suggested Answer: C

To re-encrypt all of your CMEK-protected Cloud Storage data after a key has been exposed, and to ensure future writes are protected with a new key, creating a new Cloud KMS key and a new Cloud Storage bucket is the best approach. Here's why option C is the best choice:

Re-encryption of Data:

By creating a new Cloud Storage bucket and copying all objects from the old bucket to the new bucket while specifying the new Cloud KMS key, you ensure that all data is re-encrypted with the new key.

This process effectively re-encrypts the data, removing any dependency on the compromised key.

Ensuring CMEK Protection:

Creating a new bucket and setting the new CMEK as the default ensures that all future objects written to the bucket are automatically protected with the new key.

This reduces the risk of objects being written without CMEK protection.

Deletion of Compromised Key:

Once the data has been copied and re-encrypted, the old key can be safely deleted from Cloud KMS, eliminating the risk associated with the compromised key.

Steps to Implement:

Create a New Cloud KMS Key:

Create a new encryption key in Cloud KMS to replace the compromised key.

Create a New Cloud Storage Bucket:

Create a new Cloud Storage bucket and set the default CMEK to the new key.

Copy and Re-encrypt Data:

Use the gsutil tool to copy data from the old bucket to the new bucket while specifying the new CMEK key:

gsutil -o 'GSUtil:gs_json_api_version=2' cp -r gs://old-bucket/* gs://new-bucket/

Delete the Old Key:

After ensuring all data is copied and re-encrypted, delete the compromised key from Cloud KMS.

Cloud KMS Documentation

Cloud Storage Encryption

Re-encrypting Data in Cloud Storage

by Arleen at Dec 08, 2024, 07:57 PM

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Wilda

6 months ago

Definitely leaning towards Cloud Spanner for this one!

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Dudley

6 months ago

Wait, can Cloud SQL really scale to 10 TB?

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Roosevelt

6 months ago

Secondary indexes are a must for range queries!

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Sunshine

7 months ago

I think Cloud SQL can handle this too, though.

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Janet

7 months ago

Cloud Spanner is great for horizontal scaling!

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Danica

7 months ago

I lean towards Cloud Spanner for this scenario, especially since it supports transactions and has good indexing options.

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Laila

7 months ago

I feel like using Cloud Dataflow could be useful, but I can't recall if it directly helps with optimizing range queries.

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Fletcher

7 months ago

I think Cloud SQL might not handle the scale we need for a 10-TB database, but I practiced a question about using secondary indexes with it.

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Virgilio

8 months ago

I remember that Cloud Spanner is designed for horizontal scaling, but I'm not sure if secondary indexes are the best approach for range queries.

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Marylyn

8 months ago

I'm not sure about using Cloud Dataflow to transform the data. That seems like an extra step that might not be necessary. I'll need to weigh the pros and cons of the different approaches.

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Aimee

8 months ago

I'm pretty confident I can solve this. The key is to use a database service that supports both transactions and secondary indexes, which points me towards Cloud Spanner.

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Oneida

8 months ago

Okay, I think I've got a strategy. I'll focus on the need for horizontal scaling and optimizing for range queries. That should help me narrow down the options.

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Loreta

8 months ago

Hmm, I'm a bit confused about the differences between Cloud SQL and Cloud Spanner. I'll need to review the key features of each to determine the best fit.

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Maira

8 months ago

This looks like a tricky question. I'll need to carefully consider the requirements around scaling, transactions, and optimizing for range queries.

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Sage

1 year ago

Wait, we're supposed to optimize for range queries? I thought we were designing storage for a dance club database. Guess I better re-read the question.

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Nickolas

1 year ago

Yo, what's up with these options? It's like they're trying to trick us or something. I'm just going to go with the one that sounds the most 'cloud-y'.

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Raymon

11 months ago

True, we need to optimize data for those nonkey columns.

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Arthur

11 months ago

But adding secondary indexes is important for range queries.

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Lajuana

12 months ago

Yeah, Cloud Spanner seems like a good choice for horizontal scaling.

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Arminda

12 months ago

I think option C sounds the most 'cloud-y'.

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Daisy

1 year ago

Cloud Dataflow, huh? Sounds like a lot of extra work to me. I'd rather just let Cloud Spanner do its thing and keep things simple.

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Kimberely

11 months ago

True, but it might be worth the extra work for better performance.

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Magdalene

11 months ago

But Cloud Dataflow could help optimize data for range queries on nonkey columns.

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Shawnee

12 months ago

I agree, Cloud Spanner seems like the easier option.

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Devorah

1 year ago

I'm all about that Cloud SQL life. With some secondary indexes, it can totally handle those query patterns. Plus, it's a familiar relational database, so I'm comfortable with it.

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Billy

11 months ago

User 3: I agree, Cloud SQL with secondary indexes is a solid choice for optimizing data.

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Brock

12 months ago

User 2: Yeah, Cloud SQL is familiar and can handle it well.

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Goldie

1 year ago

User 1: Cloud SQL with secondary indexes is the way to go for those query patterns.

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Keshia

1 year ago

Cloud Spanner for the win! It's the perfect choice for a 10-TB database that needs horizontal scalability and support for range queries. Secondary indexes are the way to go.

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