- A
Partition the data by date and store in separate directories for each partition.
Why wrong: While partitioning can improve query performance, it doesn't reduce storage cost; also BigLake supports partition pruning with Hive partitioning but that's not a direct cost-saving action.
- B
Configure object lifecycle management to transition files older than 90 days to Nearline storage.
Lifecycle policies automatically move data to cheaper storage classes, reducing cost.
- C
Convert all JSON files to CSV to reduce storage size.
Why wrong: CSV may be larger for nested data; JSON is fine and BigQuery supports it. Conversion adds overhead.
- D
Use BigLake to create external tables with row-level security and access delegation.
BigLake provides unified access control and enables querying Cloud Storage data directly with BigQuery.
- E
Enable Cloud KMS to encrypt the data with customer-managed encryption keys.
Why wrong: While KMS adds security, it does not directly reduce storage costs; data is already encrypted at rest.
Design a Secure and Cost-Effective Data Lake with BigLake and Cloud Storage
This PDE practice question tests your understanding of pde exam topics. 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 healthcare company stores patient records as JSON files in Cloud Storage for analysis. They want to design a data lake that enables querying the data with BigQuery while minimizing storage costs and maintaining data security. Which two actions should they take? (Choose two.)
Quick Answer
The correct actions are to use BigLake to create external tables with row-level security and access delegation, and to implement object lifecycle management to move older data to colder storage classes. BigLake bridges Cloud Storage and BigQuery by allowing direct querying of external data while enforcing fine-grained security controls like row-level filtering, which is essential for protecting sensitive patient records in a healthcare data lake. Object lifecycle management reduces costs by automatically transitioning infrequently accessed JSON files from Standard to Nearline or Coldline storage without sacrificing queryability. On the Google Professional Data Engineer exam, this question tests your understanding of how to balance security and cost optimization in a data lake architecture—a common scenario where candidates mistakenly choose encryption options or partitioning, which do not directly address cost. Remember the mnemonic “BigLake for security, Lifecycle for savings” to keep the two distinct actions straight.
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
Configure object lifecycle management to transition files older than 90 days to Nearline storage.
Options B and D are correct. Option B uses Cloud Storage object lifecycle management to transition older objects to Nearline storage, reducing storage costs while maintaining queryability via BigQuery external tables. Option D uses BigLake to create external tables with row-level security and access delegation, ensuring data security and enabling direct BigQuery querying without moving data, while minimizing storage costs by avoiding data duplication.
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.
- ✗
Partition the data by date and store in separate directories for each partition.
Why it's wrong here
While partitioning can improve query performance, it doesn't reduce storage cost; also BigLake supports partition pruning with Hive partitioning but that's not a direct cost-saving action.
- ✓
Configure object lifecycle management to transition files older than 90 days to Nearline storage.
Why this is correct
Lifecycle policies automatically move data to cheaper storage classes, reducing cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Convert all JSON files to CSV to reduce storage size.
Why it's wrong here
CSV may be larger for nested data; JSON is fine and BigQuery supports it. Conversion adds overhead.
- ✓
Use BigLake to create external tables with row-level security and access delegation.
Why this is correct
BigLake provides unified access control and enables querying Cloud Storage data directly with BigQuery.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable Cloud KMS to encrypt the data with customer-managed encryption keys.
Why it's wrong here
While KMS adds security, it does not directly reduce storage costs; data is already encrypted at rest.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that converting JSON to CSV always reduces storage size, but the primary cost-saving mechanism for infrequently accessed data is lifecycle management to colder storage tiers like Nearline. Additionally, BigLake provides security and access delegation without needing to transform the data format.
Detailed technical explanation
How to think about this question
Cloud Storage lifecycle management uses rules based on object age, creation date, or storage class to automatically transition objects to colder storage tiers like Nearline (30-day minimum), Coldline (90-day minimum), or Archive (365-day minimum). Nearline storage costs ~$0.01/GB/month compared to Standard's ~$0.02/GB/month, but retrieval incurs a data access fee. BigLake external tables can enforce row-level security via access delegation, allowing fine-grained control without duplicating data into BigQuery native storage.
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.
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?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Configure object lifecycle management to transition files older than 90 days to Nearline storage. — Options B and D are correct. Option B uses Cloud Storage object lifecycle management to transition older objects to Nearline storage, reducing storage costs while maintaining queryability via BigQuery external tables. Option D uses BigLake to create external tables with row-level security and access delegation, ensuring data security and enabling direct BigQuery querying without moving data, while minimizing storage costs by avoiding data duplication.
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 →
Keep practising
More PDE practice questions
- A company wants to process large CSV files stored in Cloud Storage and load them into BigQuery. The files are generated…
- A company runs a Dataflow streaming pipeline that reads from Cloud Pub/Sub and writes to BigQuery. The pipeline uses a s…
- A company uses Cloud Dataproc for ephemeral clusters to run batch jobs. They want to ensure job reliability and data qua…
- Your company uses Vertex AI Pipelines to automate model retraining. The pipeline has three steps: data extraction from B…
- A company wants to use BigQuery to query data stored in Parquet files in Cloud Storage without loading the data into Big…
- A company has deployed a machine learning model to AI Platform Prediction. The model uses a custom container with a Tens…
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.