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Google Associate Data Practitioner Exam - Topic 1 Question 17 Discussion

Actual exam question for Google's Associate Data Practitioner exam
Question #: 17
Topic #: 1
[All Associate Data Practitioner Questions]

Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

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Suggested Answer: C

Using Cloud Composer to create Directed Acyclic Graphs (DAGs) is the best solution because it is a fully managed, scalable workflow orchestration service based on Apache Airflow. Cloud Composer allows you to define complex task dependencies and schedules while integrating seamlessly with Google Cloud services such as Cloud Storage, BigQuery, and Dataproc for Apache Spark jobs. This approach minimizes operational overhead, supports scheduling and automation, and provides an efficient and fully managed way to orchestrate your data pipelines.

Extract from Google Documentation: From 'Cloud Composer Overview' (https://cloud.google.com/composer/docs): 'Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow, enabling you to schedule and automate complex data pipelines with dependencies across Google Cloud services like Cloud Storage, Dataproc, and BigQuery.' Reference: Google Cloud Documentation - 'Cloud Composer' (https://cloud.google.com/composer).


by Hyun at Nov 20, 2025, 03:46 AM

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Deja
2 days ago
I feel like A is too simple for this scenario.
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Brock
7 days ago
Option D could work too, but it's more complex.
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Colette
12 days ago
I agree, C makes sense. It handles dependencies well.
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Matt
17 days ago
B is good for async tasks, but not for this scenario.
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Kirk
22 days ago
D sounds cool, but isn't managing Kubernetes a hassle?
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Hassie
27 days ago
Wait, why not just use A? Seems simpler.
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Nickolas
2 months ago
Totally agree, DAGs in Cloud Composer are super effective!
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Mee
2 months ago
I think C is the best option for complex dependencies.
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Andrew
2 months ago
I'm just glad I don't have to deal with all these cloud services in my day-to-day work. Sounds like a headache!
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Nickie
2 months ago
Haha, I bet the exam writers had fun coming up with these tricky options!
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Salena
2 months ago
A) and B) are not suitable for this use case. They are more for simple job scheduling, not complex data pipelines.
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Mickie
2 months ago
D) is also a good option. Airflow on GKE provides more flexibility and control over the workflow orchestration.
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Meaghan
3 months ago
C) is the correct answer. Cloud Composer is a fully managed workflow orchestration service that can handle complex data pipelines with dependencies.
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Peggie
3 months ago
I keep mixing up Cloud Tasks and Cloud Scheduler. I think Cloud Tasks is for asynchronous processing, but I’m not confident it fits this scenario.
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Rueben
3 months ago
I practiced a similar question where we had to choose between Cloud Composer and Airflow. I think both can work, but I feel like Composer is more integrated with GCP.
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Kara
3 months ago
I remember we discussed using Cloud Composer for managing complex workflows, especially with dependencies. It seems like the right choice here.
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Aleisha
3 months ago
C seems like the easiest and most straightforward option to me. As long as the Cloud Composer operators can handle the integration with Cloud Storage, Spark, and BigQuery, that would be my top choice.
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Trina
3 months ago
Option D sounds interesting, using Airflow on GKE. That would give me more control and flexibility, but might be more complex to set up and maintain. I'd have to consider my team's capabilities.
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Georgiann
4 months ago
I think option C is the best. DAGs in Cloud Composer are perfect for this.
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Kaitlyn
4 months ago
I'm leaning towards C. The fact that Cloud Composer is fully managed is a big plus, and the ability to use the appropriate operators to connect to the different services seems really useful.
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Ivette
4 months ago
I'm not entirely sure, but I think Cloud Scheduler is more for simple tasks. This question feels like it needs something more robust.
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Arleen
5 months ago
Hmm, I'm a bit unsure between C and D. Both use DAGs to model the dependencies, but C is fully managed while D requires deploying Airflow on GKE. I'd need to weigh the tradeoffs to decide.
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Laurena
5 months ago
I think I'd go with option C. Cloud Composer seems like a good fully managed solution for orchestrating these complex data pipelines with dependencies.
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Christoper
4 months ago
Cloud Composer really simplifies the orchestration.
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King
4 months ago
I agree, option C is solid for managing dependencies.
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