Cloud Digital Leader Why cloud technology is transforming business Practice Question
A regional hospital chain wants to improve patient outcomes by analyzing electronic health records (EHRs) from multiple departments, including radiology, pathology, and pharmacy. Currently, each department stores data in separate on-premises databases, making it difficult to correlate information. The hospital must comply with HIPAA and other data privacy regulations. They have a small IT team and limited budget for new hardware. They want to enable clinicians to run ad-hoc queries across all data and generate insights using machine learning, without managing infrastructure. Which solution best achieves these goals?
⚠ Common exam trap
Google Cloud often tests the misconception that on-premises data warehouses (Option A) are the only HIPAA-compliant option, but the trap here is that cloud-native services like Cloud Healthcare API and BigQuery are fully HIPAA-eligible and actually reduce compliance burden through automated controls and managed infrastructure.
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
✓
Use Cloud Healthcare API to ingest and standardize data from each department, store in BigQuery, and use BigQuery ML to build predictive models.
It leverages the Cloud Healthcare API to ingest and standardize data from disparate on-premises databases into a unified format, stores it in BigQuery for serverless ad-hoc querying, and uses BigQuery ML to build predictive models without managing infrastructure. This fully meets HIPAA compliance through built-in data residency and access controls, while the small IT team avoids hardware procurement and maintenance overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Purchase additional on-premises servers and implement a data warehouse with ETL processes.
Why it's wrong here
Purchasing additional on-premises servers and implementing a data warehouse with ETL processes still requires building and maintaining custom pipelines to extract, transform, and load data from every department, which does not eliminate existing silos. The capital expense and ongoing IT administration are significant, and scaling for predictive analytics would require procuring and tuning additional ML infrastructure. Even after ETL, the data may remain in inconsistent formats unless extensive data governance is applied, making this an onerous, non-fully-managed path.
- ✗
Deploy a third-party analytics SaaS tool and export data from each department manually.
Why it's wrong here
Deploying a third-party analytics SaaS tool with manual exports from each department introduces compliance and operational risks. Manual exports are error-prone, create stale data, and scale poorly across departments with different formats and schedules. Moreover, unless the vendor signs a HIPAA Business Associate Agreement (BAA) and the architecture is validated for protected health information, this approach may violate regulatory requirements. It also fails to standardize the data, so the analytics will be fragmented and not enable integrated predictive modeling.
- ✗
Migrate all data to Cloud Storage and grant clinicians access to files for manual analysis.
Why it's wrong here
Migrating all data to Cloud Storage provides only object storage, which does not support SQL queries, joins, or machine learning directly on the data. Clinicians would have to download files and manually analyze them in spreadsheets or BI tools, a process that is time-consuming, inconsistent, and incapable of handling the scale or complexity of healthcare data. Without an ingestion and standardization layer like the Cloud Healthcare API, the raw files remain in heterogeneous formats, and there is no mechanism to build or operationalize predictive models for patient outcomes.
- ✓
Use Cloud Healthcare API to ingest and standardize data from each department, store in BigQuery, and use BigQuery ML to build predictive models.
Why this is correct
Using the Cloud Healthcare API to ingest and standardize data from each department addresses silos by converting disparate formats (e.g., FHIR, HL7v2, DICOM) into consistent, interoperable schemas. The standardized data is loaded into BigQuery, a fully managed, HIPAA-eligible serverless data warehouse, allowing analysts to query across the entire hospital chain without managing infrastructure. BigQuery ML enables building and deploying predictive models directly on the warehouse using SQL, avoiding the need for separate ML training environments. This combined solution is scalable, secure, and operationally efficient, making it the optimal choice for advanced analytics.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
Learn chapter
Cloud Digital Transformation
Key term
Regional
Regional refers to a deployment strategy where cloud resources are distributed across multiple geographic areas to improve availability, reduce latency, and meet compliance requirements.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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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.