- A
Set the BigQuery table schema to allow automatic addition of new fields by using 'ignore_unknown_values'.
This option tells BigQuery to ignore unknown fields and allows schema auto-detection.
- B
Configure the BigQuery table to require all fields, causing the pipeline to fail on unknown fields.
Why wrong: This would cause failures, not handle schema drift.
- C
Use ALTER TABLE ADD COLUMN DDL statements to add nullable columns for new fields.
You can proactively or reactively add nullable columns to handle new fields.
- D
Configure the Dataflow pipeline to update the BigQuery table schema when new fields are detected.
Dataflow can programmatically add new columns to the BigQuery table.
- E
Set all columns in the BigQuery table as NULLABLE from the start.
Why wrong: This does not handle new fields that are not already in the schema.
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.
You are designing a data pipeline that ingests streaming data from Pub/Sub, processes it with Dataflow, and writes to BigQuery. You need to ensure that schema changes in the incoming data (new fields) are handled without pipeline failure. Which THREE steps should you take? (Choose THREE.)
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
Set the BigQuery table schema to allow automatic addition of new fields by using 'ignore_unknown_values'.
To handle schema drift, BigQuery allows schema relaxation (adding nullable columns) via DDL. Dataflow can update the destination table schema using BigQuery APIs (e.g., set the schema to include unknown fields). Alternatively, using BigQuery's automatic schema detection with 'ignore_unknown_values' can allow new fields to be added automatically. Setting all fields to NULLABLE in advance is not practical. Setting the table to require all fields causes failures on unknown fields.
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.
- ✓
Set the BigQuery table schema to allow automatic addition of new fields by using 'ignore_unknown_values'.
Why this is correct
This option tells BigQuery to ignore unknown fields and allows schema auto-detection.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Configure the BigQuery table to require all fields, causing the pipeline to fail on unknown fields.
Why it's wrong here
This would cause failures, not handle schema drift.
- ✓
Use ALTER TABLE ADD COLUMN DDL statements to add nullable columns for new fields.
Why this is correct
You can proactively or reactively add nullable columns to handle new fields.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Configure the Dataflow pipeline to update the BigQuery table schema when new fields are detected.
Why this is correct
Dataflow can programmatically add new columns to the BigQuery table.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set all columns in the BigQuery table as NULLABLE from the start.
Why it's wrong here
This does not handle new fields that are not already in the schema.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
- →
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: Set the BigQuery table schema to allow automatic addition of new fields by using 'ignore_unknown_values'. — To handle schema drift, BigQuery allows schema relaxation (adding nullable columns) via DDL. Dataflow can update the destination table schema using BigQuery APIs (e.g., set the schema to include unknown fields). Alternatively, using BigQuery's automatic schema detection with 'ignore_unknown_values' can allow new fields to be added automatically. Setting all fields to NULLABLE in advance is not practical. Setting the table to require all fields causes failures on unknown fields.
What should I do if I get this PDE question wrong?
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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.