1Z0-184-25 Question 102
Single answerYou have a 200 GB text corpus stored in an Autonomous Database and need to generate vector embeddings using a third-party NLP library that is not available within the database. Your goal is to run the workload outside of the database in a scalable and cost-effective manner, then store the resulting embeddings in OCI Object Storage for downstream analysis. Which Oracle Cloud Infrastructure (OCI) service offers a fully managed Spark environment to address this requirement?
- A
Use OCI Data Flow with a custom Spark application to read data from the database and write embeddings to Object Storage
- B
Create a custom container image in Oracle Container Engine for Kubernetes (OKE) to run the third-party library in a continuous deployment pipeline
- C
Use the built-in Oracle Database Machine Learning capabilities to run the third-party library directly inside the DB
- D
Provision an OCI Compute VM and manually install Spark, the third-party library, and other dependencies
Show answer and explanation
Correct answer: A
Explanation
OCI Data Flow is designed for scalable, serverless batch processing with Spark. It simplifies running large-scale data transformations and machine learning workloads, including generating vector embeddings, outside the Oracle Database environment. For more details, refer to the official OCI Data Flow documentation on deploying custom Spark jobs with external libraries and storing results in OCI Object Storage.
- A. Correct.
Option 1: Correct. OCI Data Flow provides a serverless, fully managed Spark environment. It is ideal for large-scale processing, including generating vector embeddings outside the database. You can configure and run your NLP code within a Spark application, scale horizontally as needed, and write the output to OCI Object Storage.
- B. Incorrect.
Option 2: Incorrect. While OKE can run containers with the NLP library, it does not offer a fully serverless Spark environment out of the box. You would have to manage cluster scaling and job orchestration, which adds complexity when compared to OCI Data Flow� managed Spark solution.
- C. Incorrect.
Option 3: Incorrect. The built-in Oracle Database Machine Learning features do not directly support external NLP libraries that are not certified for use within the database environment. This option also contradicts the requirement to run the workload outside of the database.
- D. Incorrect.
Option 4: Incorrect. While provisioning an OCI Compute VM and installing Spark manually is feasible, it is not a fully managed or serverless approach. You would be responsible for infrastructure setup, scaling, and maintenance, which is more complex than using OCI Data Flow.