Steve Sanders Steve Sanders's Profile Page

Steve Sanders Steve Sanders

0 Course Enrolled • 0 Course Completed

Biography

Oracle 1Z0-184-25 Prüfung Übungen und Antworten

Als ein Mitglied der IT-Branche, machen Sie sich noch Sorgen um die Oracle 1Z0-184-25 IT-Zertifizierungsprüfungen? Es ist nicht so leicht, die Oracle 1Z0-184-25 IT-Zertifizierungsprüfung, die Ihre relevanten Fachkenntnisse und Fähigkeiten überprüft, zu bestehen. Für die Kandidaten, die sich zum ersten Mal an der Oracle 1Z0-184-25 IT-Zertifizierungsprüfung beteiligen, ist ein zielgerichtetes Schulungsprogramm von großer Notwendigkeit. Zertpruefung stellt den Kandidaten die zielgerichteten Programme, die Simulationsprüfung, zielgerichtete Lernhilfe und die Prüfungsfragen und Antworten, die 95% der realen Prüfung ähnlich sind, zur Verfügung. Schicken Sie doch schnell Zertpruefung in den Warenkorb.

Oracle 1Z0-184-25 Prüfungsplan:

Thema
Einzelheiten

Thema 1

Performing Similarity Search: This section tests the skills of Machine Learning Engineers in conducting similarity searches to find relevant data points. It includes performing exact and approximate similarity searches using vector indexes. Candidates will also work with multi-vector similarity search to handle searches across multiple documents for improved retrieval accuracy.

Thema 2

Using Vector Indexes: This section evaluates the expertise of AI Database Specialists in optimizing vector searches using indexing techniques. It covers the creation of vector indexes to enhance search speed, including the use of HNSW and IVF vector indexes for performing efficient search queries in AI-driven applications.

Thema 3

Understand Vector Fundamentals: This section of the exam measures the skills of Data Engineers in working with vector data types for storing embeddings and enabling semantic queries. It covers vector distance functions and metrics used in AI vector search. Candidates must demonstrate proficiency in performing DML and DDL operations on vectors to manage data efficiently.

Thema 4

Using Vector Embeddings: This section measures the abilities of AI Developers in generating and storing vector embeddings for AI applications. It covers generating embeddings both inside and outside the Oracle database and effectively storing them within the database for efficient retrieval and processing.

Thema 5

Building a RAG Application: This section assesses the knowledge of AI Solutions Architects in implementing retrieval-augmented generation (RAG) applications. Candidates will learn to build RAG applications using PL
SQL and Python to integrate AI models with retrieval techniques for enhanced AI-driven decision-making.

>> 1Z0-184-25 Lerntipps <<

1Z0-184-25 Exam & 1Z0-184-25 Übungsmaterialien

Nach den Forschungen in den letzten Jahren sind die Fragen und Antworten zur Oracle 1Z0-184-25 Zertifizierungsprüfung von Zertpruefung den realen Prüfung sehr ähnlich. Zertpruefung verspricht, dass Sie zum ersten Mal die Oracle 1Z0-184-25 (Oracle AI Vector Search Professional) Zertifizierungsprüfung 100% bestehen können.

Oracle AI Vector Search Professional 1Z0-184-25 Prüfungsfragen mit Lösungen (Q31-Q36):

31. Frage
What security enhancement is introduced in Exadata System Software 24ai?

A. Enhanced encryption algorithm for data at rest
B. SNMP security (Security Network Management Protocol)
C. Integration with third-party security tools

Antwort: A

Begründung:
Exadata System Software 24ai (noted in context beyond 23ai) introduces an enhanced encryption algorithm for data at rest (B), strengthening security for stored data, including vectors. Third-party integration (A) isn't highlighted as a 24ai feature. SNMP security (C) relates to network monitoring, not a primary Exadata enhancement. Oracle's Exadata documentation for 24ai emphasizes advanced encryption as a key security upgrade.

32. Frage
Which Python library is used to vectorize text chunks and the user's question in the following example?
import oracledb
connection = oracledb.connect(user=un, password=pw, dsn=ds)
table_name = "Page"
with connection.cursor() as cursor:
create_table_sql = f"""
CREATE TABLE IF NOT EXISTS {table_name} (
id NUMBER PRIMARY KEY,
payload CLOB CHECK (payload IS JSON),
vector VECTOR
)"""
try:
cursor.execute(create_table_sql)
except oracledb.DatabaseError as e:
raise
connection.autocommit = True
from sentence_transformers import SentenceTransformer
encoder = SentenceTransformer('all-MiniLM-L12-v2')

A. json
B. oci
C. sentence_transformers
D. oracledb

Antwort: C

Begründung:
In the provided Python code, the sentence_transformers library (A) is imported and used to instantiate a SentenceTransformer object with the 'all-MiniLM-L12-v2' model. This library is designed to vectorize text (e.g., chunks and questions) into embeddings, a common step in RAG applications. The oracledb library (C) handles database connectivity, not vectorization. oci (B) is for OCI service interaction, not text embedding. json (D) processes JSON data, not vectors. The code explicitly uses sentence_transformers for vectorization, consistent with Oracle's examples for external embedding integration.

33. Frage
If a query vector uses a different distance metric than the one used to create the index, whathappens?

A. The index automatically updates
B. A warning is logged, but the query executes
C. An exact match search is triggered
D. The query fails

Antwort: D

Begründung:
In Oracle Database 23ai, vector indexes (e.g., HNSW, IVF) are built with a specific distance metric (e.g., cosine, Euclidean) that defines how similarity is computed. If a query specifies a different metric (e.g., querying with Euclidean on a cosine-based index), the index cannot be used effectively, and the query fails (A) with an error, as the mismatch invalidates the index's structure. An exact match search (B) doesn't occur automatically; Oracle requires explicit control. The index doesn't update itself (C), and warnings (D) are not the default behavior-errors are raised instead. Oracle's documentation mandates metric consistency for index usage.

34. Frage
Which PL/SQL function converts documents such as PDF, DOC, JSON, XML, or HTML to plain text?

A. DBMS_VECTOR_CHAIN.UTL_TO_TEXT
B. DBMS_VECTOR.CONVERT_TO_TEXT
C. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS
D. DBMS_VECTOR.TEXT_TO_PLAIN

Antwort: A

Begründung:
In Oracle Database 23ai, DBMS_VECTOR_CHAIN.UTL_TO_TEXT is the PL/SQL function that converts documents in formats like PDF, DOC, JSON, XML, or HTML into plain text, a key step in preparing data for vectorization in RAG workflows. DBMS_VECTOR.TEXT_TO_PLAIN (A) is not a valid function. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS (C) splits text into smaller segments, not converts documents. DBMS_VECTOR.CONVERT_TO_TEXT (D) does not exist in the documented packages. UTL_TO_TEXT is part of the DBMS_VECTOR_CHAIN package, designed for vector processing pipelines, and is explicitly noted for document conversion in Oracle's documentation.

35. Frage
You are tasked with creating a table to store vector embeddings with the following characteristics: Each vector must have exactly 512 dimensions, and the dimensions should be stored as 32-bitfloating point numbers. Which SQL statement should you use?

A. CREATE TABLE vectors (id NUMBER, embedding VECTOR)
B. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32))
C. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512))
D. CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8))

Antwort: B

Begründung:
In Oracle 23ai, the VECTOR data type can specify dimensions and precision. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32)) (D) defines a column with exactly 512 dimensions and FLOAT32 (32-bit float) format, meeting both requirements. Option A omits the format (defaults vary), risking mismatch. Option B is unspecified, allowing variable dimensions-not "exactly 512." Option C uses INT8, not FLOAT32, and '*' denotes undefined dimensions. Oracle's SQL reference confirms this syntax for precise VECTOR definitions.

36. Frage
......

Ist es nicht einfach, die Oracle 1Z0-184-25 Zertifizierungsprüfung zu bestehen? Es ist sehr wahrscheinlich, Prüfung einmalig zu bestehen, wenn Sie die Fragenkataloge zur Oracle 1Z0-184-25 aus Zertpruefung wählen. Die Fragenkataloge zur Oracle 1Z0-184-25 aus Zertpruefung sind die Sammlung von den höchsten zertifizierten Experten im Oracle -Bereich und das Ergebnis von Innovation, sie haben absolute Autorität. Wählen Sie Zertpruefung, bereuen Sie niemals.

1Z0-184-25 Exam: https://www.zertpruefung.de/1Z0-184-25_exam.html

Steve Sanders Steve Sanders

Biography

Support