1Z0-1110-26日本語版試験勉強法、1Z0-1110-26復習範囲

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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: OCI Data Science Service	30%	- Model catalog 1. Model versioning 2. Model metadata - Projects and notebooks 1. Notebook sessions 2. Conda environments
Topic 2: MLOps and OCI Integration	20%	- Automation and pipelines 1. Model lifecycle management 2. CI/CD integration - OCI ecosystem 1. Object Storage 2. IAM and security
Topic 3: Model Development and Deployment	30%	- Model training 1. Experiments 2. Hyperparameter optimization - Model deployment 1. Deployment creation 2. Prediction endpoints
Topic 4: Machine Learning Fundamentals	20%	- Supervised learning 1. Classification 2. Regression - Unsupervised learning 1. Dimensionality reduction 2. Clustering

>> 1Z0-1110-26日本語版試験勉強法 <<

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Oracle Cloud Infrastructure Data Science Professional 認定 1Z0-1110-26 試験問題 (Q141-Q146):

質問 # 141
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image dat a. Which of the following THREE are possible ways to annotate an image in Data Labeling?

A. Adding labels to an image using object detection, by drawing bounding boxes to an image
B. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image
C. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
D. Adding multiple labels to an image
E. Adding a single label to an image

正解：A、D、E

質問 # 142
Which of these options allow the sharing and loading back of ML models into a notebook session?

A. Model deployment
B. Model provenance
C. Model taxonomy
D. Model catalog

正解：D

解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify the mechanism for sharing and reloading ML models in OCI Data Science.
Evaluate Options:
A . Model provenance: Tracks model origin—informative but not a sharing mechanism.
B . Model taxonomy: Categorizes models (e.g., regression)—not for sharing/loading.
C . Model deployment: Makes models accessible as endpoints, not for notebook reloading.
D . Model catalog: Stores models and artifacts, enabling sharing and loading into sessions.
Reasoning: The Model Catalog is OCI’s centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK).
Conclusion: D is the correct tool.
The OCI Model Catalog “enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation.” Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".

質問 # 143
Which statement about logs for Oracle Cloud Infrastructure Jobs is true?

A. All stdout and stderr are automatically stored when automatic log creation is enabled
B. Integrating data science jobs resources with logging is mandatory
C. Each job run sends outputs to a single log for that job
D. Logs are automatically deleted when the job and job run is deleted

正解：A

解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify a true statement about OCI Jobs logging.
Understand Logging: Jobs can log stdout/stderr to OCI Logging service.
Evaluate Options:
A: False—Each run has its own log, not a single job log.
B: False—Logging is optional, not mandatory.
C: True—When enabled, stdout/stderr are auto-captured.
D: False—Logs persist unless explicitly deleted.
Reasoning: C matches OCI’s automatic logging feature.
Conclusion: C is correct.
OCI documentation states: “When automatic log creation is enabled for Data Science Jobs, all stdout and stderr outputs are captured and stored in the OCI Logging service.” A is incorrect (per-run logs), B is optional, and D contradicts log retention—only C is accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs Logging".

質問 # 144
You have been given a collection of digital files required for a business audit. They consist of several different formats that you would like to annotate using Oracle Cloud Infrastructure (OCI) Data Labeling. Which THREE types of files could this tool annotate?

A. Images of computer server racks
B. Video footage of a conversation in a conference room
C. A typewritten document that details an annual budget
D. A collection of purchase orders for office supplies
E. An audio recording of a phone conversation

正解：A、B、C

解説：
Detailed Answer in Step-by-Step Solution:
Understand OCI Data Labeling Capabilities: OCI Data Labeling is designed to annotate data for machine learning, supporting specific file types like images, text documents, and videos.
Evaluate Options:
A . Video footage: Supported for tasks like object detection or action recognition.
B . Images: Supported for image classification, object detection, etc.
C . Typewritten document: Supported as text data for tasks like entity extraction or classification.
D . Purchase orders: While potentially text-based, this is ambiguous without format clarification (e.g., PDF, image). OCI supports text annotation, but “purchase orders” isn’t a specific file type—it’s assumed as text here.
E . Audio recording: Not supported, as OCI Data Labeling focuses on visual and textual data, not audio.
Select Three: A (video), B (images), and C (text documents) are explicitly supported file types.
OCI Data Labeling supports annotating datasets of images, text, and videos, as per the official documentation. Video footage (A) can be annotated for tasks like object tracking, images (B) for classification or detection, and typewritten documents (C) for text-based annotations (e.g., named entity recognition). Audio files (E) are not supported, and while purchase orders (D) could be text, the question specifies “typewritten document” as a clearer match. (Oracle Cloud Infrastructure Data Labeling Service Documentation, "Supported Data Types").

質問 # 145
You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS-supported data formats?

A. XML
B. DOCX
C. Pandas DataFrame
D. Raw Images
E. JSON

正解：A、C、E

解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify three data formats supported by ADS SDK for loading data.
Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.
Evaluate Options:
A . DOCX: Not natively supported—requires conversion (e.g., to text).
B . Pandas DataFrame: Supported—core format for data manipulation in ADS.
C . JSON: Supported—common structured data format.
D . Raw Images: Not directly supported—image data needs preprocessing (e.g., via Vision).
E . XML: Supported—parseable structured format.
Reasoning: ADS focuses on tabular/structured data—B, C, E align; A and D require external handling.
Conclusion: B, C, E are correct.
OCI documentation states: “ADS SDK’s DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions.” DOCX (A) isn’t natively handled, and raw images (D) require preprocessing outside ADS—B, C, E match the supported list.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".

質問 # 146
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1Z0-1110-26復習範囲: https://www.passtest.jp/Oracle/1Z0-1110-26-shiken.html