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?

AUse Cloud Scheduler to schedule the jobs to run.

BUse Cloud Tasks to schedule and run the jobs asynchronously.

CCreate directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

DCreate directed acyclic graphs (DAGs) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

Show Suggested Answer

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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Marg

1 month ago

C is definitely the way to go for automation and scheduling.

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Aileen

1 month ago

B seems like it could miss dependencies. Not ideal.

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Deja

2 months ago

I feel like A is too simple for this scenario.

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Brock

2 months ago

Option D could work too, but it's more complex.

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Colette

2 months ago

I agree, C makes sense. It handles dependencies well.

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Matt

2 months ago

B is good for async tasks, but not for this scenario.

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Kirk

2 months ago

D sounds cool, but isn't managing Kubernetes a hassle?

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Hassie

2 months ago

Wait, why not just use A? Seems simpler.

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Nickolas

3 months ago

Totally agree, DAGs in Cloud Composer are super effective!

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Mee

3 months ago

I think C is the best option for complex dependencies.

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Andrew

3 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

4 months ago

Haha, I bet the exam writers had fun coming up with these tricky options!

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Salena

4 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

4 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

4 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

4 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

4 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

5 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

5 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

5 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

5 months ago

I think option C is the best. DAGs in Cloud Composer are perfect for this.

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Kaitlyn

6 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

6 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

6 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

6 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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