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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Oracle Database Actions and Data Studio Tools | 15% | - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks - Describe Database Actions and core development tools |
| Topic 2: Working with JSON and Graph in Oracle AI Database | 20% | - Distinguish when graph capabilities and Property Graph Views fit a business use case - Explain JSON and Oracle AI Database JSON capabilities - Describe core graph concepts and graph analytic capabilities |
| Topic 3: Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Determine how AI Vector Search supports GenAI pipelines and RAG - Apply AI Vector Search to combined semantic and business-data search scenarios - Describe Select AI in Autonomous AI Database |
| Topic 4: Building Low-Code Applications and Agentic AI | 10% | - Describe Oracle APEX as Oracle's low-code platform - Choose the appropriate Agent Factory capability for a no-code AI agent use case |
| Topic 5: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Create an Autonomous AI Database Serverless instance for a basic workload |
| Topic 6: Working with AI and Vector Foundations | 15% | - Explain vectors, embeddings, and the Oracle VECTOR data type - Describe AI, AGI, and machine learning foundations - Apply vector distance and indexing concepts to similarity search needs |
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質問 # 25
A business wants one data platform where semantic similarity, relational consistency, and SQL-based filtering all work together.
Which statement aligns with this design goal?
正解：B
質問 # 26
A vector index will not fit entirely in memory. Which index organization option should be considered for use?
正解：C
解説：
NEIGHBOR PARTITIONS is the correct index organization when an entirely memory-resident vector graph is unsuitable. The uploaded assessment identifies NEIGHBOR PARTITIONS as the intended answer.
Oracle AI Vector Search distinguishes two primary approximate vector-index organizations: INMEMORY NEIGHBOR GRAPH , based on HNSW, and NEIGHBOR PARTITIONS , based on IVF.
HNSW is specifically an in-memory graph structure. Oracle documentation describes HNSW indexes as specialized memory-only structures and provides vector-memory-pool facilities for holding them. By contrast, the IVF-based Neighbor Partition index organizes vectors into centroid-based partitions and narrows each approximate search to relevant partitions rather than maintaining the complete graph as an in-memory HNSW structure.
EXACT SEARCH ONLY is not an index organization and would typically require evaluating a broader candidate set, sacrificing the scalability benefits of approximate indexing. TARGET ACCURACY is a parameter governing the accuracy/performance trade-off of approximate searches, not an index organization.
INMEMORY NEIGHBOR GRAPH directly conflicts with the stated memory constraint.
Study Guide reference: Working with AI and Vector Foundations - vector index organizations, IVF
/Neighbor Partitions, HNSW/In-Memory Neighbor Graph, and approximate similarity search.
質問 # 27
A team must deploy an Oracle Autonomous AI Database Serverless instance so that database access is available only inside a private OCI network.
Which access setting should they use?
正解：B
解説：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Private endpoint access only is the correct network setting when an Autonomous AI Database Serverless instance must be reachable only through a private OCI network. Oracle documents that this option assigns a private endpoint, private IP address, and hostname and allows traffic only from the specified VCN, including supported peered or connected private networks. Public access is blocked unless explicitly enabled through separate advanced configuration. "Secure access from allowed IPs and VCNs only" is an ACL-based public- endpoint model and can permit approved public addresses, so it does not satisfy the stricter private-only requirement. "Secure access from everywhere" is broader still. Therefore, option D precisely matches the requirement to keep database access inside the private OCI network.
質問 # 28
A data engineer is creating a vector similarity query and wants to choose the distance metric correctly.
Which guidance should be applied?
正解：C
解説：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Oracle recommends using the distance metric associated with the embedding model that generated the vectors. Different metrics-such as cosine, Euclidean, dot product, Manhattan, or Hamming-measure similarity in different ways, and an embedding model is normally trained or intended to be evaluated with a particular metric. Oracle's AI Vector Search documentation states that it is generally best to match the query distance metric to the metric used to train the embedding model. Oracle also notes that a vector index should be created and searched with the appropriate distance function; using a different function can prevent index use and trigger exact search behavior. Table row count, maintenance schedules, and the presence of JSON attributes do not determine semantic vector geometry. Therefore, option C is the correct guidance. Oracle Docs
質問 # 29
A manufacturer needs graph analysis that reflects inserts and updates from operational tables immediately.
How does a Property Graph View support this requirement?
正解：C
解説：
A Property Graph View provides a graph interpretation directly over data stored in relational database tables rather than requiring a separately maintained copy of that data. Oracle documents the Property Graph View as a view-like object containing metadata describing vertices, edges, labels, keys, and properties. Because the underlying graph information remains in the referenced relational tables, modifications to those tables are immediately reflected when the graph is queried.
This architecture directly satisfies the manufacturer's requirement. When operational applications insert or update rows in the underlying objects, graph queries subsequently operate against the updated relational data through the graph metadata definition. There is no required intermediate conversion to JSON and no scheduled batch refresh of a separate graph copy. Oracle also supports direct graph queries against database- resident property graph structures, including pattern matching through database graph-query facilities.
A separate in-memory graph server can be used for certain advanced analytics scenarios, but that does not change what a database Property Graph View fundamentally provides. The key exam distinction is metadata- based graph representation over existing database objects versus duplicated graph storage requiring synchronization. Therefore, option D precisely matches the required real-time operational behavior.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - Property Graph Views, metadata-based graph modeling, and relational-table integration.
質問 # 30
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