- A
Use the gcloud dataflow jobs update command with the new Flex Template.
Dataflow supports updating a running streaming job from a Flex Template by specifying --update and the job ID. This allows code changes without draining.
- B
Stop the pipeline, update the template, and restart with the same job name.
Why wrong: Stopping causes data loss unless using drain; restarting loses checkpointed state. The requirement is to update without draining.
- C
Modify the original template and redeploy it as a new job with the same pipeline name.
Why wrong: Redeploying as a new job creates a separate pipeline; the original continues running. You cannot update a running job by launching a new one.
- D
Use the gcloud dataflow jobs drain command, then restart with the new template.
Why wrong: Draining stops the pipeline gracefully but violates the requirement of updating without draining.
PDE Maintaining and Automating Data Workloads Practice Question
This PDE practice question tests your understanding of maintaining and automating data workloads. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company runs a streaming Dataflow pipeline that reads from Pub/Sub, enriches data with a side input from BigQuery, and writes to BigQuery. After updating the pipeline code (adding a new field to the output), the engineer notices that the new pipeline version is not picking up the updated code because the job was started from a template. The engineer wants to update the streaming pipeline without draining it. What should the engineer do?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use the gcloud dataflow jobs update command with the new Flex Template.
Option A is correct because the `gcloud dataflow jobs update` command allows you to update a running streaming Dataflow pipeline with a new Flex Template without draining or stopping the job. This command performs an in-place update, preserving the job's state and checkpointing, so the pipeline continues processing with the new code. Since the original job was started from a template, using this command with the new Flex Template ensures the updated code is picked up seamlessly.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the gcloud dataflow jobs update command with the new Flex Template.
Why this is correct
Dataflow supports updating a running streaming job from a Flex Template by specifying --update and the job ID. This allows code changes without draining.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Stop the pipeline, update the template, and restart with the same job name.
Why it's wrong here
Stopping causes data loss unless using drain; restarting loses checkpointed state. The requirement is to update without draining.
- ✗
Modify the original template and redeploy it as a new job with the same pipeline name.
Why it's wrong here
Redeploying as a new job creates a separate pipeline; the original continues running. You cannot update a running job by launching a new one.
- ✗
Use the gcloud dataflow jobs drain command, then restart with the new template.
Why it's wrong here
Draining stops the pipeline gracefully but violates the requirement of updating without draining.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that you must drain or stop a streaming pipeline to update it, but the `gcloud dataflow jobs update` command is specifically designed for in-place updates of streaming jobs started from templates.
Detailed technical explanation
How to think about this question
Under the hood, the `gcloud dataflow jobs update` command leverages Dataflow's streaming update mechanism, which uses savepoints (checkpoints) to preserve the state of the pipeline (e.g., Pub/Sub offsets, side input caches) and then replaces the job graph with the new template's graph. This is similar to Apache Flink's savepoint-based upgrades. A real-world scenario is when you need to add a new field to a BigQuery output schema without losing the current streaming position; the update command ensures exactly-once semantics during the transition.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Maintaining and Automating Data Workloads — study guide chapter
Learn the concepts, then practise the questions
- →
Maintaining and Automating Data Workloads practice questions
Targeted practice on this topic area only
- →
All PDE questions
1,000 questions across all exam domains
- →
Google Professional Data Engineer study guide
Full concept coverage aligned to exam objectives
- →
PDE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PDE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Designing Data Processing Systems practice questions
Practise PDE questions linked to Designing Data Processing Systems.
Ingesting and Processing the Data practice questions
Practise PDE questions linked to Ingesting and Processing the Data.
Storing the Data practice questions
Practise PDE questions linked to Storing the Data.
Preparing and Using Data for Analysis practice questions
Practise PDE questions linked to Preparing and Using Data for Analysis.
Maintaining and Automating Data Workloads practice questions
Practise PDE questions linked to Maintaining and Automating Data Workloads.
Building and operationalizing data processing systems practice questions
Practise PDE questions linked to Building and operationalizing data processing systems.
Operationalizing machine learning models practice questions
Practise PDE questions linked to Operationalizing machine learning models.
Ensuring solution quality practice questions
Practise PDE questions linked to Ensuring solution quality.
PDE fundamentals practice questions
Practise PDE questions linked to PDE fundamentals.
PDE scenario practice questions
Practise PDE questions linked to PDE scenario.
PDE troubleshooting practice questions
Practise PDE questions linked to PDE troubleshooting.
Practice this exam
Start a free PDE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this PDE question test?
Maintaining and Automating Data Workloads — This question tests Maintaining and Automating Data Workloads — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the gcloud dataflow jobs update command with the new Flex Template. — Option A is correct because the `gcloud dataflow jobs update` command allows you to update a running streaming Dataflow pipeline with a new Flex Template without draining or stopping the job. This command performs an in-place update, preserving the job's state and checkpointing, so the pipeline continues processing with the new code. Since the original job was started from a template, using this command with the new Flex Template ensures the updated code is picked up seamlessly.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.