- A
Use a one-time full dump and import to avoid complexity.
Why wrong: A one-time dump would likely exceed the one-month window or require downtime; incremental approach is better.
- B
Use BigQuery Data Transfer Service to connect directly to Snowflake.
Why wrong: BigQuery Data Transfer Service does not support Snowflake as a source.
- C
Export data from Snowflake to Cloud Storage in Parquet format.
Data is exported from Snowflake to Cloud Storage (e.g., via Snowflake's COPY INTO command).
- D
Load data from Cloud Storage into BigQuery using batch load jobs.
Data is loaded from GCS into BigQuery using batch jobs.
- E
Convert Snowflake SQL to BigQuery SQL for all queries and views.
SQL dialect differences need to be addressed to ensure compatibility.
Question 85 of 980
PCD Migrate Data Solutions Practice Question
This PCD practice question tests your understanding of migrate data solutions. 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 is migrating a 2 TB Snowflake data warehouse to BigQuery. The migration must be completed within a month with no downtime. Which THREE actions should the team take?
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
✓
Export data from Snowflake to Cloud Storage in Parquet format.
Option C is correct because exporting Snowflake data to Cloud Storage in Parquet format is an efficient, columnar format that reduces storage costs and speeds up loading into BigQuery. Parquet is natively supported by BigQuery and allows for schema inference, making the migration process seamless. This approach also supports incremental updates if needed, avoiding downtime by keeping the source operational during the export.
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 a one-time full dump and import to avoid complexity.
Why it's wrong here
A one-time dump would likely exceed the one-month window or require downtime; incremental approach is better.
- ✗
Use BigQuery Data Transfer Service to connect directly to Snowflake.
Why it's wrong here
BigQuery Data Transfer Service does not support Snowflake as a source.
- ✓
Export data from Snowflake to Cloud Storage in Parquet format.
Why this is correct
Data is exported from Snowflake to Cloud Storage (e.g., via Snowflake's COPY INTO command).
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Load data from Cloud Storage into BigQuery using batch load jobs.
Why this is correct
Data is loaded from GCS into BigQuery using batch jobs.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Convert Snowflake SQL to BigQuery SQL for all queries and views.
Why this is correct
SQL dialect differences need to be addressed to ensure compatibility.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that BigQuery Data Transfer Service can connect to any database, but it only supports specific Google and third-party sources, not Snowflake; candidates may also mistakenly think a full dump is simpler, ignoring the no-downtime constraint.
Detailed technical explanation
How to think about this question
Under the hood, exporting to Parquet leverages Snowflake's UNLOAD command to write compressed, partitioned files to Cloud Storage, which BigQuery can load in parallel using batch load jobs with automatic partitioning and clustering. This method also supports schema evolution and can be integrated with Cloud Composer or Dataflow for orchestration, ensuring data consistency across the 2 TB dataset within the one-month timeline. A real-world scenario might involve using Snowflake's TIME_TRAVEL feature to capture a consistent snapshot without pausing writes.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
- →
Migrate Data Solutions — study guide chapter
Learn the concepts, then practise the questions
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FAQ
Questions learners often ask
What does this PCD question test?
Migrate Data Solutions — This question tests Migrate Data Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Export data from Snowflake to Cloud Storage in Parquet format. — Option C is correct because exporting Snowflake data to Cloud Storage in Parquet format is an efficient, columnar format that reduces storage costs and speeds up loading into BigQuery. Parquet is natively supported by BigQuery and allows for schema inference, making the migration process seamless. This approach also supports incremental updates if needed, avoiding downtime by keeping the source operational during the export.
What should I do if I get this PCD 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
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Last reviewed: Jul 4, 2026
This PCD 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 PCD exam.
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