PROFESSIONAL-CLOUD-DEVELOPER · Question #30
Case Study 1 - HipLocal Company Overview HipLocal is a community application designed to facilitate communication between people in close proximity. It is used for event planning and organizing sporti
The correct answer is B. Use the Cloud Data Loss Prevention API for de-identification of the review dataset.. The Cloud Data Loss Prevention (DLP) API (B) is the correct service for handling sensitive data in datasets. De-identification is the right technique here - it transforms PII (names, emails, phone numbers, etc.) into a protected form that preserves data utility for analysis while
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Case Study 1 - HipLocal Company Overview HipLocal is a community application designed to facilitate communication between people in close proximity. It is used for event planning and organizing sporting events, and for businesses to connect with their local communities. HipLocal launched recently in a few neighborhoods in Dallas and is rapidly growing into a global phenomenon. Its unique style of hyper-local community communication and business outreach is in demand around the world. Executive Statement We are the number one local community app; it's time to take our local community services global. Our venture capital investors want to see rapid growth and the same great experience for new local and virtual communities that come online, whether their members are 10 or 10000 miles away from each other. Solution Concept HipLocal wants to expand their existing service, with updated functionality, in new regions to better serve their global customers. They want to hire and train a new team to support these regions in their time zones. They will need to ensure that the application scales smoothly and provides clear uptime data. Existing Technical Environment HipLocal's environment is a mix of on-premises hardware and infrastructure running in Google Cloud Platform. The HipLocal team understands their application well, but has limited experience in global scale applications. Their existing technical environment is as follows:
- Existing APIs run on Compute Engine virtual machine instances hosted in GCP.
- State is stored in a single instance MySQL database in GCP.
- Data is exported to an on-premises Teradata/Vertica data warehouse.
- Data analytics is performed in an on-premises Hadoop environment.
- The application has no logging.
- There are basic indicators of uptime; alerts are frequently fired when the APIs are unresponsive.
Business Requirements HipLocal's investors want to expand their footprint and support the increase in demand they are seeing. Their requirements are:
- Expand availability of the application to new regions.
- Increase the number of concurrent users that can be supported.
- Ensure a consistent experience for users when they travel to different regions.
- Obtain user activity metrics to better understand how to monetize their product.
- Ensure compliance with regulations in the new regions (for example, GDPR).
- Reduce infrastructure management time and cost.
- Adopt the Google-recommended practices for cloud computing.
Technical Requirements
- The application and backend must provide usage metrics and monitoring.
- APIs require strong authentication and authorization.
- Logging must be increased, and data should be stored in a cloud analytics platform.
- Move to serverless architecture to facilitate elastic scaling.
- Provide authorized access to internal apps in a secure manner.
HipLocal's data science team wants to analyze user reviews. How should they prepare the data?
Options
- AUse the Cloud Data Loss Prevention API for redaction of the review dataset.
- BUse the Cloud Data Loss Prevention API for de-identification of the review dataset.
- CUse the Cloud Natural Language Processing API for redaction of the review dataset.
- DUse the Cloud Natural Language Processing API for de-identification of the review dataset.
How the community answered
(40 responses)- A13% (5)
- B78% (31)
- C8% (3)
- D3% (1)
Explanation
The Cloud Data Loss Prevention (DLP) API (B) is the correct service for handling sensitive data in datasets. De-identification is the right technique here - it transforms PII (names, emails, phone numbers, etc.) into a protected form that preserves data utility for analysis while removing the ability to identify individuals. Redaction (A) completely removes sensitive tokens, making that data useless for analysis. The Cloud Natural Language API (C, D) analyzes text for sentiment, entities, and syntax - it has no capability for PII detection, redaction, or de-identification. The DLP API is specifically designed for these privacy-protection operations.
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