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Exam Professional Cloud Developer All Questions

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Exam Professional Cloud Developer topic 1 question 130 discussion

Actual exam question from Google's Professional Cloud Developer
Question #: 130
Topic #: 1
[All Professional Cloud Developer Questions]

You have an HTTP Cloud Function that is called via POST. Each submission's request body has a flat, unnested JSON structure containing numeric and text data. After the Cloud Function completes, the collected data should be immediately available for ongoing and complex analytics by many users in parallel. How should you persist the submissions?

  • A. Directly persist each POST request's JSON data into Datastore.
  • B. Transform the POST request's JSON data, and stream it into BigQuery.
  • C. Transform the POST request's JSON data, and store it in a regional Cloud SQL cluster.
  • D. Persist each POST request's JSON data as an individual file within Cloud Storage, with the file name containing the request identifier.
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️
by ParagSanyashiv at Jan. 9, 2022, 10:08 a.m.

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ParagSanyashiv
Highly Voted 2 years, 10 months ago
Selected Answer: B
B should be the correct one because question has mentioned for analytics of the data.
upvoted 15 times
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thewalker
Most Recent 4 months ago
Selected Answer: B
The best answer here is B. Transform the POST request's JSON data and stream it into BigQuery. Here's why: BigQuery's Strengths for Analytics: BigQuery is specifically designed for large-scale, complex analytics. It offers: Scalability: Handles massive datasets with ease. Parallel Processing: Allows many users to query the data concurrently without performance degradation. SQL Support: Provides a familiar and powerful query language for data exploration. Data Exploration Tools: Comes with built-in tools for data visualization and analysis.
upvoted 1 times
thewalker
4 months ago
Why the other options are less suitable: A. Datastore: Datastore is a NoSQL database, great for storing structured data, but not ideal for complex analytics. It's not designed for the kind of parallel querying and data exploration that BigQuery excels at. C. Cloud SQL: While Cloud SQL can handle analytics, it's generally more expensive than BigQuery for large-scale operations. It's also not as optimized for parallel queries. D. Cloud Storage: Cloud Storage is great for storing files, but it's not a database. You'd need to build your own analytics infrastructure on top of it, which would be more complex and less efficient than using BigQuery.
upvoted 1 times
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__rajan__
1 year, 2 months ago
Selected Answer: B
B is correct since we need to do complex analytics.
upvoted 1 times
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purushi
1 year, 3 months ago
Selected Answer: B
The key here is "Collected data should be IMMEDIATELY available for ongoing and complex analytics", and hence option B is correct.
upvoted 1 times
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Pime13
1 year, 9 months ago
Selected Answer: B
"data should be immediately available for ongoing and complex analytics" -> B
upvoted 1 times
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omermahgoub
1 year, 10 months ago
Selected Answer: B
B. Transform the POST request's JSON data, and stream it into BigQuery. BigQuery is a highly scalable data warehouse that is well suited for handling large amounts of data and complex analytics in near real-time. By streaming the JSON data from your Cloud Function directly into BigQuery, you can make the collected data immediately available for analytics by many users in parallel. BigQuery support various data types including json, so you can store your request body without any transformation. A. Directly persist each POST request's JSON data into Datastore. Datastore is a NoSQL document database that can be used to store structured data, but it's not designed to handle the high volume of data that you need to analyze in near real-time. And it would require additional processing to be available for analysis.
upvoted 1 times
omermahgoub
1 year, 10 months ago
C. Transform the POST request's JSON data, and store it in a regional Cloud SQL cluster. Cloud SQL is a fully-managed MySQL, PostgreSQL, and SQL Server database service, which is more suited for transactional workloads, rather than for storing large amounts of data for analytics purposes.
upvoted 1 times
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omermahgoub
1 year, 10 months ago
Storing the data as individual files in Cloud Storage may not be the best approach for immediate and parallel analytics as it would require additional processing and data manipulation to make it available for analytics purposes.
upvoted 1 times
omermahgoub
1 year, 10 months ago
D. Persist each POST request's JSON data as an individual file within Cloud Storage, with the file name containing the request identifier.
upvoted 1 times
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omermahgoub
1 year, 10 months ago
A. Directly persist each POST request's JSON data into Datastore. Datastore is a NoSQL document database that can be used to store structured data, but it's not designed to handle the high volume of data that you need to analyze in near real-time. And it would require additional processing to be available for analysis.
upvoted 1 times
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zellck
1 year, 11 months ago
Selected Answer: B
B is the answer.
upvoted 1 times
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tomato123
2 years, 3 months ago
Selected Answer: B
B is correct
upvoted 2 times
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GCPCloudArchitectUser
2 years, 9 months ago
Option B… can’t disagree enough Analytics = BigQuery
upvoted 2 times
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Blueocean
2 years, 10 months ago
Is between Option B and D. Option B talks about transforming JSON data but no where in question we get to understand this need. So even though is BigQuery for analytics purposes Option D is more suitable.
upvoted 2 times
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