CertJukenの1Z0-1110-26問題集は、印刷して使えるPDF版、本番環境を再現するデスクトップ版、ブラウザで手軽に取り組めるオンライン版の3つの形態を用意しています。Oracle Cloud Infrastructure Data Science Professionalの対策を、自分の学習スタイルに合った形で進められます。

Oracle 1Z0-1110-26 試験概要:

認定ベンダー:	Oracle
試験名:	Oracle Cloud Infrastructure 2026 Data Science Professional
試験番号:	1Z0-1110-26
認定の有効期間:	最新の年間試験バージョンに基づくOracle認定資格は、対応する試験バージョンが終了するまで有効です。
合格点:	68%
関連資格:	Oracle Cloud Infrastructure Data Science Professional Oracle Cloud Infrastructure Foundations Associate Oracle Cloud Infrastructure AI Foundations Associate
対応言語:	英語
出題数:	50
試験形式:	選択式
試験時間:	90 分
受験料:	USD $245
推奨トレーニング:	Oracle Learning Explorer Oracle Cloud Infrastructure Data Science Professionalラーニング・パス
受験申し込み:	Oracle認定ポータル Oracle University試験ページ
サンプル問題:
受験方法:	Oracle Universityを通じて、オンライン監督試験および認定テストセンター（利用可能な場合）で受験可能です。
前提条件:	必須の前提条件はありません。Oracle Cloud Infrastructure Data Scienceの実務経験と、機械学習の概念に関する知識があることが推奨されます。
公式シラバスのURL:	https://education.oracle.com/es/ouexam-pexam_1z0-1110-26/pexam_1Z0-1110-26

Oracle 1Z0-1110-26 試験シラバストピック:

Oracle Cloud Infrastructure Data Science Professionalの疑問をまとめたQ&A

Oracle Cloud Infrastructure Data Science Professional 認定 1Z0-1110-26 試験問題：

問題 #1

Why is data sampling useful for data scientists?

A. It lets them analyze datasets in small batches to reduce their use of system resources.
B. It enables them to use a representative subset of data to build accurate analytical models more quickly.
C. It reduces the amount of data storage space that’s required for data science applications.

問題 #2

You realize that your model deployment is about to reach its utilization limit. What would you do to avoid the issue before requests start to fail? Which THREE steps would you perform?

A. Delete the deployment
B. Update the deployment to use a larger virtual machine (more CPUs/memory)
C. Update the deployment to add more instances
D. Update the deployment to use fewer instances
E. Reduce the load balancer bandwidth limit so that fewer requests come in

問題 #3

As a data scientist, you create models for cancer prediction based on mammographic images. The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix. Which model would you prefer and why?
Model 1 has Test accuracy is 80% and recall is 70%
Model 2 has Test accuracy is 75% and recall is 85%

A. Model 2, because recall has more impact on predictions in this use case
B. Model 1, because the test accuracy is high
C. Model 2, because recall is high
D. Model 1, because recall has lesser impact on predictions in this use case

問題 #4

You are a data scientist building a pipeline in the Oracle Cloud Infrastructure (OCI) Data Science service for your machine learning project. You want to optimize the pipeline completion time by running some steps in parallel. Which statement is true about running pipeline steps in parallel?

A. All pipeline steps are always run in parallel
B. Pipeline steps can be run in sequence or in parallel, as long as they create a directed acyclic graph (DAG)
C. Steps in a pipeline can be run only sequentially
D. Parallel steps cannot be run if they are completely independent of each other

問題 #5

You are building a model and need input that represents data as morning, afternoon, or evening. However, the data contains a timestamp. What part of the Data Science lifecycle would you be in when creating the new variable?

A. Model type selection
B. Data access
C. Model validation
D. Feature engineering

解説：