1Z0-184-25 exam dumps: questions 101 to 182 of 182

Correct answer: A

• A. Correct.

  CORRECT. By using Oracle Data Science with a GPU shape, you can run custom Python or machine learning frameworks to generate embeddings outside the database. Storing embeddings in Object Storage first and then loading them into Oracle Database ensures a secure and decoupled workflow. This approach offloads the training workload from the database, leverages OCI� managed data science environment, and preserves data integrity.

• B. Incorrect.

  INCORRECT. Oracle Database In-Memory accelerates queries rather than generating vector embeddings from raw text. It does not automatically convert text data into vectors for semantic search.

• C. Incorrect.

  INCORRECT. MySQL HeatWave ML is an ML solution integrated into MySQL, not Oracle Database. Generating embeddings using a MySQL-based service cannot directly populate an Oracle Database� vector table without significant custom integration and is not the recommended approach for Oracle Database workloads.

• D. Incorrect.

  INCORRECT. Oracle ASM primarily manages storage for Oracle Database but does not provide a mechanism to generate vector embeddings. This method would also place a heavy computational load on the same server running the database, contrary to the goal of offloading GPU-intensive processing.

Correct answer: A

• 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.

Correct answer: A

• A. Correct.

  Option 1 is correct. Leveraging a scalable environment with GPU acceleration (e.g., OCI Data Science, OCI Container Engine for Kubernetes) allows efficient generation of embeddings using your custom model. Storing them in Object Storage, then loading them in batches or through data ingestion pipelines into the database, helps maintain performance and synchronization. Oracle documentation recommends generating resource-intensive embeddings outside the database and then ingesting the results for vector queries.

• B. Incorrect.

  Option 2 is incorrect. Installing external deep learning libraries and running the generation within stored procedures in Autonomous Database is not recommended due to potential performance overhead, maintenance complexity, and limitations in library support. The scenario explicitly requires generation outside the database.

• C. Incorrect.

  Option 3 is incorrect. While Oracle Functions can be used for certain tasks, relying solely on triggered functions and manual copy commands to synchronize the embeddings with the database is inefficient and prone to errors. This approach lacks effective batching and might not scale for high volumes of data.

• D. Incorrect.

  Option 4 is incorrect. Oracle GoldenGate for Big Data is useful for data replication between heterogeneous systems, but it is not typically designed for generating embeddings or handling large-scale compute-intensive inference tasks. It also wouldn't solve the requirement for external GPU-based processing.

Correct answers: A, B

• A. Correct.

  Correct. Leveraging Oracle Data Science or GPU-enabled Compute shapes lets you offload the embedding generation workload from the database. Storing embeddings temporarily on block volumes and loading them via asynchronous or scheduled jobs helps avoid unnecessary overhead and maintains scalability.

• B. Correct.

  Correct. Batching documents outside the database and writing the generated embeddings directly to the database is a best practice for handling large workloads. This approach reduces contention and ensures a clean separation of concerns between the inference pipeline and the database.

• C. Incorrect.

  Incorrect. Running an external embedding API for each row insertion from a stored procedure would introduce significant overhead, degrade insert performance, and complicate transaction management, making it unsuitable for large-scale or high-throughput scenarios.

• D. Incorrect.

  Incorrect. Oracle Autonomous Database does not currently offer built-in GPU support for generating vector embeddings inside the database. Embedding generation should occur outside, then be stored in the database for efficient queries and indexing.

Correct answers: A, C

• A. Correct.

  Correct. Oracle Data Science is well-suited for large-scale machine learning tasks. Running a custom Python script (potentially on GPU shapes) lets you efficiently handle large data stored in Object Storage, generate embeddings, and store those embeddings externally or load them back into the Oracle Database once completed.

• B. Incorrect.

  Incorrect. While Oracle Functions can handle smaller tasks and stateless operations effectively, it is not typically recommended for large-scale batch embeddings (e.g., 50 million records). Functions have limited memory and execution time, making them less optimal for large, long-running operations.

• C. Correct.

  Correct. Oracle AI Services � Language can generate text embeddings in batch, making it feasible to process larger datasets outside the database. You can then store the embeddings in the Oracle Database for further similarity searches or analytics.

• D. Incorrect.

  Incorrect. This explicitly generates embeddings inside the database, which contradicts the stated requirement to do it outside the database for performance reasons. Additionally, relying solely on in-database procedures can introduce higher overhead for large-scale data processing.