1Z0-184-25 Question 71
Select 2You are designing a media portal on Oracle Cloud Infrastructure (OCI) 2025 to store and manage millions of user-uploaded images. The goal is to detect and prevent duplicate uploads by performing an EXACT similarity search at scale. Which two methods are valid best practices in OCI for implementing this kind of exact match detection?
- A
Compute a cryptographic hash (e.g., MD5 or SHA-256) for each image and store it in the database with a function-based or unique index to quickly find duplicates
- B
Leverage the ETag value from the Object Storage for each uploaded object to detect identical files and store these ETags in the database for comparisons
- C
Use the Oracle Database vector search index with an approximate distance metric to identify near-duplicate images
- D
Rely on Oracle Data Labeling service to automatically identify and label all duplicate file uploads
Show answer and explanation
Correct answers: A, B
Explanation
When designing an exact similarity search solution, focusing on cryptographic hashes (like MD5 or SHA-256) or leveraging ETag values (if stored in OCI Object Storage) are common and recommended best practices. Oracle� Object Storage ETag is designed to reflect file content (assuming standard, non-multi-part uploads), and storing these values alongside a hash in the database helps ensure a robust check for duplicates. In contrast, approximate vector searches and labeling services are better suited for near-duplicate or category-based searches. Refer to Oracle� documentation on OCI Object Storage ETags and database indexing strategies for further details.
- A. Correct.
Correct. Using a cryptographic hash is a straightforward and reliable way to find exact matches. Storing the hash in a column that is indexed allows fast queries to check if the hash already exists. This approach is widely used in duplicate detection pipelines.
- B. Correct.
Correct. In Oracle Object Storage, the ETag typically provides a hash-based signature of the file contents. By storing and indexing ETags in a relational table, you can quickly check for exact file matches. If a user re-uploads the same content (without multi-part complexities), the ETag remains the same, enabling duplicate detection.
- C. Incorrect.
Incorrect. Vector search with approximate distance metrics is designed for near-duplicate or similarity-based queries. It is useful for finding images similar to a query image but not for guaranteed exact matches.
- D. Incorrect.
Incorrect. Oracle Data Labeling helps annotate and categorize data but does not automatically detect perfectly duplicated objects on its own. It is not a substitute for hash-based or ETag-based exact comparisons.