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1Z0-184-25試験対応 & 1Z0-184-25試験感想

当社の1Z0-184-25テストトレントは、課題に取り組み、Oracle AI Vector Search Professional試験に合格するのに役立つ新しい方法を探し続けます。当社の優れたパフォーマンスにより、世界有数の国際試験銀行として認められるために、当社のOracle AI Vector Search Professional認定試験は長い間集中しており、教材の設計に多くのリソースと経験を蓄積してきました。 Oracle AI Vector Search Professional試験証明書の取得を支援します。私たちは心からあなたが私たちを信頼し、選択することを心から願っています。

Oracleの1Z0-184-25認定試験を除いて、最近非常に人気がある試験はまたOracle、Cisco、IBM、SAPなどの様々な認定試験があります。しかし、もし1Z0-184-25認証資格を取りたいなら、Fast2testの1Z0-184-25問題集はあなたを願望を達成させることができます。試験の受験に自信を持たないので諦めることをしないでください。Fast2testの試験参考書を利用することを通して自分の目標を達成することができますから。1Z0-184-25認証資格を入手してから、他のIT認定試験を受験することもできます。Fast2testの試験問題集を手にすると、どのような試験でも問題ではありません。

>> 1Z0-184-25試験対応 <<

1Z0-184-25試験感想 & 1Z0-184-25技術試験

最も短い時間で自分のIT技能を増強したいけれど、質の良い学習教材がないので悩んでいますか。ご心配なく、Fast2testのOracleの1Z0-184-25試験トレーニング資料を手に入れるなら、ITに関する認定試験はなんでも楽に合格できます。Fast2testの Oracleの1Z0-184-25試験トレーニング資料は高度に認証されたIT領域の専門家の経験と創造を含めているものです。Fast2testは君にとって、ベストな選択だといっても良いです。

Oracle 1Z0-184-25 認定試験の出題範囲：

トピック
出題範囲

トピック 1

Leveraging Related AI Capabilities: This section evaluates the skills of Cloud AI Engineers in utilizing Oracle’s AI-enhanced capabilities. It covers the use of Exadata AI Storage for faster vector search, Select AI with Autonomous for querying data using natural language, and data loading techniques using SQL Loader and Oracle Data Pump to streamline AI-driven workflows.

トピック 2

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.

トピック 3

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.

Oracle AI Vector Search Professional 認定 1Z0-184-25 試験問題 (Q41-Q46):

質問 # 41
What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?

A. HNSW guarantees accuracy, whereas IVF sacrifices performance for accuracy
B. Both operate identically but differ in memory usage
C. HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions
D. HNSW is partition-based, whereas IVF uses neighbor graphs for indexing

正解：C

質問 # 42
If a query vector uses a different distance metric than the one used to create the index, whathappens?

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

正解：C

解説：
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.

質問 # 43
When generating vector embeddings for a new dataset outside of Oracle Database 23ai, which factor is crucial to ensure meaningful similarity search results?

A. The storage format of the new dataset (e.g., CSV, JSON)
B. The physical location where the vector embeddings are stored
C. The same vector embedding model must be used for vectorizing the data and creating a query vector
D. The choice of programming language used to process the dataset (e.g., Python, Java)

正解：C

解説：
Meaningful similarity search relies on the consistency of the vector space in which embeddings reside. Vector embeddings are generated by models (e.g., BERT, SentenceTransformer) that map data into a high-dimensional space, where proximity reflects semantic similarity. If different models are used for the dataset and query vector, the embeddings will be in incompatible spaces, rendering distance metrics (e.g., cosine, Euclidean) unreliable. The programming language (A) affects implementation but not the semantic consistency of embeddings-Python or Java can use the same model equally well. The physical storage location (B) impacts accessibility and latency but not the mathematical validity of similarity comparisons. The storage format (C) influences parsing andingestion but does not determine the embedding space. Oracle 23ai's vector search framework explicitly requires the same embedding model for data and queries to ensure accurate results, a principle that applies universally, even outside the database.

質問 # 44
Which is a characteristic of an approximate similarity search in Oracle Database 23ai?

A. It compares every vector in the dataset
B. It trades off accuracy for faster performance
C. It is slower than exact similarity search
D. It always guarantees 100% accuracy

正解：B

解説：
Approximate similarity search (ANN) in Oracle 23ai (B) uses indexes (e.g., HNSW, IVF) to trade accuracy for speed, returning near-matches faster by not comparing all vectors. Exact search compares every vector (A), not ANN. It doesn't guarantee 100% accuracy (C); that's exact search. It's faster, not slower (D), than exact search due to indexing. Oracle's documentation defines ANN's speed-accuracy trade-off as its hallmark.

質問 # 45
What happens when querying with an IVF index if you increase the value of the NEIGHBOR_PARTITIONS probes parameter?

A. Index creation time is reduced
B. Accuracy decreases
C. More partitions are probed, improving accuracy, but also increasing query latency
D. The number of centroids decreases

正解：C

解説：
The NEIGHBOR_PARTITIONS parameter in Oracle 23ai's IVF index controls how many partitions are probed during a query. Increasing this value examines more clusters, raising theprobability of finding relevant vectors, thus improving accuracy (recall). However, this increases computational effort, leading to higher query latency-a classic ANN trade-off. The number of centroids (A) is fixed during index creation and unaffected by query parameters. Accuracy does not decrease (B); it improves. Index creation time (C) is unrelated to query-time settings. Oracle's documentation on IVF confirms that NEIGHBOR_PARTITIONS directly governs this accuracy-latency balance.

質問 # 46
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私たちは現在、競争の激しい世界に住んでいます。 1Z0-184-25認定を取得するなど、ソフトパワーを改善する以外に選択肢はありません。 1Z0-184-25トレントが試験に合格し、履歴書を強調することで職場で成功を収めることができます。 1Z0-184-25試験に合格して認定資格を取得したい場合は、1Z0-184-25ガイドの質問があなたの理想的な選択であることを確認できます。当社は、1Z0-184-25試験問題に関する専門チーム、高品質のサービス、リーズナブルな価格を提供します。

1Z0-184-25試験感想: https://jp.fast2test.com/1Z0-184-25-premium-file.html