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Google Associate Data Practitioner Exam - Topic 2 Question 15 Discussion

You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to addressthe problem. You need to ensure that the final output is generated as quickly as possible. What should you do?
D) Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
A) Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.
B) Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
C) Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.

Google Associate Data Practitioner Exam - Topic 2 Question 15 Discussion

Actual exam question for Google's Associate Data Practitioner exam
Question #: 15
Topic #: 2
[All Associate Data Practitioner Questions]

You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address

the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

Show Suggested Answer Hide Answer
Suggested Answer: D

Using Cloud Composer to design the processing pipeline as a Directed Acyclic Graph (DAG) is the most suitable approach because:

Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.

Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output of one stage serves as input to the next.

Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading to faster final output generation.


by Gianna at Aug 01, 2025, 08:05 PM

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Mammie
6 months ago
I agree, B is definitely the way to go for efficiency!
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Tamesha
6 months ago
D seems like the best choice for managing dependencies.
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Jerrod
7 months ago
Option B sounds solid for handling errors.
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Magdalene
7 months ago
I think A is a bit slow with the user input part.
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Ettie
7 months ago
Wait, can you really jump stages in C? That seems risky.
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Beula
8 months ago
I recall that using Cloud Workflows allows for more flexibility in rerunning stages, but I’m not entirely clear on how to implement the input parameter logic.
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Millie
8 months ago
I practiced a similar question about error handling in Spark, but I feel like waiting for user input might slow things down too much.
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Erick
8 months ago
I think using Cloud Composer could be a solid option since it manages workflows well, but I’m a bit confused about how to clear the state of a failed task.
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Mignon
8 months ago
I remember we discussed how Dataflow is great for handling failures and can restart from specific points, but I'm not sure if it’s the best choice here.
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Layla
9 months ago
Option C with Cloud Workflow could be a good solution. Being able to jump to a specific stage based on an input parameter could make it easier to address errors.
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Deonna
9 months ago
I'm leaning towards Option D with Cloud Composer. The ability to clear the state of a failed task and rerun the workflow seems really useful.
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Keneth
9 months ago
I think the key here is to design a pipeline that can handle failures and restart efficiently. Option B with Dataflow seems like a good choice.
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Jose
9 months ago
Hmm, I'm a bit confused by the question. I'm not sure which approach would be the best for this scenario.
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Adelaide
9 months ago
This looks like a tricky question. I'll need to think carefully about the different options and their pros and cons.
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Tasia
10 months ago
I'd go with Option B. Dataflow's 'retry' feature is like the undo button for your data pipeline. Brilliant!
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Cristal
6 months ago
Option A seems too manual. I prefer automation!
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Mariann
6 months ago
I wonder if Option D would be better for complex workflows.
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Trina
7 months ago
Agreed! The retry feature really saves time.
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Dick
7 months ago
I like Option B too! Dataflow makes it so easy to manage failures.
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Layla
11 months ago
Option A? Really? Waiting for user input? That's so 1990s. Let's keep this cloud-native, folks!
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Melvin
10 months ago
D) Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
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Rima
10 months ago
B) Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
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Billy
11 months ago
I think option B sounds like a good approach.
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Mila
11 months ago
Option D seems like the most elegant solution. A DAG in Cloud Composer would give me better visibility and control over the entire process.
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Mica
10 months ago
That's a good point too. Using Dataflow for the pipeline design can also be a reliable option for processing the data files.
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Bo
10 months ago
I think I would go with option B. Designing the pipeline as a set of PTransforms in Dataflow seems like a straightforward approach.
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Garry
10 months ago
I agree, having a directed acyclic graph in Cloud Composer would definitely help in managing the data processing stages efficiently.
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Josphine
10 months ago
Option D seems like the most elegant solution. A DAG in Cloud Composer would give me better visibility and control over the entire process.
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Nakita
11 months ago
I'm leaning towards Option C. The flexibility of being able to jump to a specific stage in the workflow could really come in handy when troubleshooting issues.
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Rolland
11 months ago
I agree, Option C seems like the most efficient way to handle errors in the pipeline.
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Levi
11 months ago
Option C sounds like a good choice. Being able to jump to a specific stage in the workflow can save a lot of time.
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Kristofer
11 months ago
Option B sounds like the way to go. Dataflow's ability to restart the pipeline after fixing errors seems like the most efficient approach.
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Avery
10 months ago
I agree, using PTransforms in Dataflow allows for easy correction of errors and quick restart of the pipeline.
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Lisandra
11 months ago
Option B sounds like the way to go. Dataflow's ability to restart the pipeline after fixing errors seems like the most efficient approach.
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