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Google Professional Data Engineer Exam - Topic 5 Question 88 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 88
Topic #: 5
[All Professional Data Engineer Questions]

You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?

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Suggested Answer: D

Dialogflow is a conversational AI platform that allows for easy implementation of chatbots without needing to code. It has built-in integration for both text and voice input via APIs like Cloud Speech-to-Text. Defining intents and entity types allows you to map common queries and keywords to responses. This would provide a low/no-code way to quickly build and iteratively improve the chatbot capabilities.

https://cloud.google.com/dialogflow/docs Dialogflow is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into your mobile app, web application, device, bot, interactive voice response system, and so on. Using Dialogflow, you can provide new and engaging ways for users to interact with your product. Dialogflow can analyze multiple types of input from your customers, including text or audio inputs (like from a phone or voice recording). It can also respond to your customers in a couple of ways, either through text or with synthetic speech.


by Major at Jul 07, 2024, 10:50 AM

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Jacqueline
3 months ago
Not sure about A, how secure is that view really?
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Cristina
3 months ago
Wait, can you really keep costs low with option D?
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Catalina
4 months ago
C seems like a lot of extra work for sharing data.
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Muriel
4 months ago
I think B is better for keeping data current.
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Art
4 months ago
Option A sounds like a smart way to control access.
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Catarina
4 months ago
Option D sounds efficient, but I wonder if the Cloud Dataflow job would be too complex for just sharing data.
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Cruz
5 months ago
I feel like option C might be too much work since creating a separate dataset could lead to data management issues.
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Chantell
5 months ago
I remember practicing a question similar to this, and I think option B could lead to higher costs with regular exports.
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Sueann
5 months ago
I think option A makes sense since it allows for controlled access without duplicating data, but I'm not entirely sure if it keeps costs low.
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Billye
5 months ago
I'm leaning towards option D with Cloud Dataflow. That seems like it would give me the most control over the data freshness and distribution. But I'll need to make sure I understand how to set that up properly.
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Felicidad
5 months ago
Hmm, I'm a bit unsure about this one. I'm trying to weigh the pros and cons of each approach to keep the costs low and ensure the data is current. I might need to re-read the question a few times.
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Gregoria
5 months ago
This seems like a straightforward question about data sharing options in BigQuery. I think I'll go with option A - creating an authorized view to control access.
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Jody
5 months ago
Option B with Cloud Storage export sounds like a good way to keep the data up-to-date, but I'm not sure if that's the most cost-effective solution. I'll have to think through the tradeoffs carefully.
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Angelo
5 months ago
Hmm, I'm a bit unsure about this one. The multiplicity notation is throwing me off. I'll need to think it through carefully.
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Sunny
5 months ago
Hmm, this seems like a tricky one. I'll need to think through the key characteristics of link state routing protocols to figure out the right answer.
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Ocie
5 months ago
Okay, let's see. The question is asking what happens if we create a global policy that conflicts with an existing service-specific policy. I'm pretty sure the Policy Centralization pattern is supposed to resolve those kinds of conflicts, so option A doesn't seem right.
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Sherman
10 months ago
Option A: The authorized view. It's like a bouncer for your data - keeps the riff-raff out while letting the VIPs in. Simple and elegant, like a well-tailored suit.
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Kaitlyn
8 months ago
A) The authorized view. It's like a bouncer for your data - keeps the riff-raff out while letting the VIPs in. Simple and elegant, like a well-tailored suit.
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Ivan
9 months ago
B) Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.
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Heike
9 months ago
A) Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
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Rasheeda
10 months ago
Option D with Dataflow sounds like the most complex solution, but it could be the most robust. Automatic data refreshes and the ability to write to different destinations? Sign me up!
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Amie
9 months ago
Using Cloud Scheduler to export data regularly to Cloud Storage might be a cost-effective way to provide access to the dataset for third-party companies.
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Tawny
9 months ago
I think creating an authorized view on the BigQuery table could be a simpler solution to control data access for third-party companies.
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Glynda
10 months ago
Option D with Dataflow sounds like the most complex solution, but it could be the most robust. Automatic data refreshes and the ability to write to different destinations? Sign me up!
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Glen
11 months ago
Option C is intriguing, but creating a separate dataset just for sharing seems overkill. I'd rather keep everything in one place if possible.
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Flo
9 months ago
Option C is intriguing, but creating a separate dataset just for sharing seems overkill. I'd rather keep everything in one place if possible.
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Cecil
9 months ago
C) Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
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Celestine
10 months ago
B) Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.
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Nickolas
10 months ago
A) Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
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Claudio
11 months ago
I see both points, but I think option B could also be a good solution to keep costs low.
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Rasheeda
11 months ago
I'm not a fan of Option B. Exporting to Cloud Storage and managing access to the bucket sounds like a hassle. Plus, how do you ensure the data is always up-to-date?
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Craig
10 months ago
C) Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
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Shawna
10 months ago
A) Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
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Trinidad
10 months ago
D) Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.
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Renea
10 months ago
I agree, Option A or C would be better. It's easier to manage access and ensure the data is current.
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Deeanna
10 months ago
C) Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
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Geraldo
11 months ago
A) Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
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Garry
11 months ago
I disagree, I believe option D is more efficient as it ensures the data is current.
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Willow
11 months ago
Option A seems like the easiest way to control access and keep the data current. No need to mess with exporting or Dataflow jobs.
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Carol
11 months ago
I think option A is the best choice because it allows us to control data access.
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