Cloud Digital Leader Practice Question: Google Cloud products, services, and solutions
A healthcare organization needs to store and analyze large volumes of patient diagnostic imaging data (e.g., DICOM files) in Google Cloud. The data must be stored in a cost-effective manner for long-term retention, with the ability to query metadata and run analytics using SQL-like queries. Which combination of Google Cloud services best meets these requirements?
⚠ Common exam trap
Google Cloud often tests the misconception that 'cost-effective long-term storage' must use Archive storage, ignoring that Nearline is sufficient for data accessed occasionally (e.g., quarterly analytics) and that BigQuery is the only service listed that provides native SQL analytics on metadata at scale.
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
✓
Cloud Storage (Nearline) for images, BigQuery for metadata analytics
Cloud Storage Nearline provides cost-effective long-term storage for large imaging files with retrieval flexibility, while BigQuery enables SQL-based analytics on metadata extracted from DICOM headers, meeting both retention and query requirements without the cost of standard storage or the complexity of transactional databases.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Storage (Standard) for images, Datastore for metadata
Why it's wrong here
Cloud Storage Standard is priced for frequently accessed data, so storing rarely accessed images in it incurs unnecessary storage costs over the long retention periods typical in healthcare. Datastore is a NoSQL document database that does not support SQL-based analytical queries such as aggregations and JOINs, making it unsuitable for metadata analytics. Moreover, Datastore's per-operation pricing and index limitations hamper large-scale scans, further reducing its viability.
- ✗
Cloud Storage (Archive) for images, Cloud Spanner for metadata
Why it's wrong here
Archive storage has a 365-day minimum retention period and high data retrieval fees, so it becomes cost-prohibitive unless images are accessed almost never, which is unlikely for a healthcare archive where occasional retrieval for audits or re-diagnosis occurs. Cloud Spanner is a globally distributed, strongly consistent relational database that bills per node and is designed for transactional workloads, not analytical queries; using it for metadata analytics would be expensive and inefficient. This combination fails to provide a cost-effective, analytics-optimized solution.
- ✓
Cloud Storage (Nearline) for images, BigQuery for metadata analytics
Why this is correct
Cloud Storage Nearline offers a low storage price for data accessed less than once per quarter, with no minimum retention duration and reasonable retrieval costs, making it ideal for storing long-term medical images that are rarely retrieved but must be retained for compliance. BigQuery is a serverless, columnar data warehouse that supports standard SQL, enabling fast aggregation, JOINs, and full scans of metadata like accession numbers and timestamps without managing infrastructure. Its pay-per-query pricing and separation of storage from compute keep costs aligned with actual analytical usage, making this combination both cost-effective and analytically powerful.
- ✗
Cloud Filestore for images, Cloud SQL for metadata
Why it's wrong here
Cloud Filestore is a managed network file system intended for compute-heavy workloads like HPC, not for serving millions of object image files; it has limited total capacity and a higher price per GB than object storage, and accessing images through file mounts is inefficient. Cloud SQL is a managed OLTP database that is not built for large-scale analytical queries; its single-instance architecture and limited I/O capacity cause performance bottlenecks when running complex aggregations over millions of metadata rows. Thus, both components are mismatched to the stated requirements for large-scale image storage and metadata analytics.
Go deeper
Related to this question
Learn chapter
Cloud Digital Transformation
Key term
SQL
SQL is a standardized programming language used to manage and manipulate relational databases, enabling querying, updating, and data retrieval.
Key term
Metadata
Metadata is data that describes other data, providing context such as when a file was created, who created it, or its size.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This GCDL 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 GCDL exam.