mediumMultiple Select
PDE Practice Question: A healthcare company stores patient records as…
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.)
⚠ Common exam trap
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.
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.
Option B is correct because configuring Object Lifecycle Management to transition objects older than 90 days to Nearline storage lowers storage costs for infrequently accessed historical patient records while keeping them queryable, since Nearline has a 30-day minimum storage duration and is cheaper than Standard. Option D is correct because BigLake external tables let BigQuery query JSON files directly in Cloud Storage without ingestion, and BigLake supports fine-grained security such as row-level security and access delegation (via BigQuery delegated access to the underlying Cloud Storage objects), satisfying the data security requirement. Option A is not required: partitioning by date in separate directories is a performance/cost optimization for query pruning, but it does not by itself minimize storage cost or provide security, and BigLake can query the data without this layout. Option C is not appropriate: converting JSON to CSV does not reliably reduce storage size and loses the nested schema, and it is not needed for BigQuery querying. Option E is not the best fit: Cloud KMS with CMEK encrypts data at rest but does not enable BigQuery querying or reduce storage costs, and Cloud Storage is already encrypted by default with Google-managed keys.
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
Partitioning directories alone does not reduce BigQuery query cost or storage cost, and BigQuery cannot query Cloud Storage JSON directly without an external table or load; the stem needs columnar Parquet plus partition pruning. Partitioning is correct for Hive-style layouts on compute engines that prune directories, not for this BigQuery design.
- ✓
Configure object lifecycle management to transition files older than 90 days to Nearline storage.
Why this is correct
Object lifecycle management automatically transitions JSON objects to Nearline after 90 days, cutting storage cost while keeping data queryable. This satisfies the cost-minimisation constraint without deleting records, and Nearline's 30-day minimum retention suits the healthcare retention profile.
- ✗
Convert all JSON files to CSV to reduce storage size.
Why it's wrong here
CSV is row-based, so BigQuery scans every column and cannot prune, raising query cost; it also loses nested JSON structure and does not reduce Cloud Storage cost meaningfully. CSV suits simple flat exports loaded into relational tables, not a query-optimised, security-conscious data lake.
- ✓
Use BigLake to create external tables with row-level security and access delegation.
Why this is correct
BigLake external tables query Cloud Storage JSON directly from BigQuery, avoiding data duplication, while row-level security and access delegation enforce per-user patient-record access. This satisfies both the querying requirement and the data-security constraint for sensitive healthcare data.
- ✗
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.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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