Easy1 markMultiple Choice
GCP PCA · Question 11 · Technical Requirements
CASE STUDY: HealthData Inc
Overview:
Industry: Healthcare Analytics
Size: 1000 employees
Environment:
- Co-located data center
- Hadoop cluster
- SFTP servers
- 50 TB patient data
Requirements:
- ML models for diagnostics
- Secure data sharing portals
- Break data silos
Exec Statements:
- CEO: Need compute for ML.
- CRO: HIPAA compliance is top priority.
- CTO: Managed services needed to replace Hadoop.
Tech Reqs:
- Strict HIPAA compliance
- Automated PHI de-identification
- Comprehensive audit logging
- CMEK
- Network isolation (no public internet)
Constraints:
- US data sovereignty
- 7-year retention (immutable)
- Easy auditor access
QUESTION: To replace the on-premises Hadoop cluster with a managed service while minimizing migration effort, which GCP service should you recommend?
CASE STUDY: HealthData Inc
Overview:
Industry: Healthcare Analytics
Size: 1000 employees
Environment:
- Co-located data center
- Hadoop cluster
- SFTP servers
- 50 TB patient data
Requirements:
- ML models for diagnostics
- Secure data sharing portals
- Break data silos
Exec Statements:
- CEO: Need compute for ML.
- CRO: HIPAA compliance is top priority.
- CTO: Managed services needed to replace Hadoop.
Tech Reqs:
- Strict HIPAA compliance
- Automated PHI de-identification
- Comprehensive audit logging
- CMEK
- Network isolation (no public internet)
Constraints:
- US data sovereignty
- 7-year retention (immutable)
- Easy auditor access
QUESTION: To replace the on-premises Hadoop cluster with a managed service while minimizing migration effort, which GCP service should you recommend?
Answer options:
A.
Cloud Dataflow
B.
Cloud Dataproc
C.
BigQuery
D.
Compute Engine with custom Hadoop installation
How to approach this question
Identify the GCP managed service that runs Apache Hadoop and Spark ecosystems.
Full Answer
B.Cloud Dataproc✓ Correct
Cloud Dataproc
Cloud Dataproc is Google's fully managed Apache Spark and Hadoop service. It allows organizations to migrate existing Hadoop workloads to the cloud quickly without rewriting them, while Google handles cluster provisioning, management, and scaling.
Common mistakes
Choosing Dataflow (A) because it's 'modern', ignoring the constraint to minimize migration effort for existing Hadoop jobs.
Practice the full GCP Professional Cloud Architect Practice Exam 6
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