有効な1z0-1110-25テスト解答Oracle 1z0-1110-25試験PDF問題を試そう
Oracle 1z0-1110-25認定リアル2026年最新の模擬試験合格させます
Oracle 1z0-1110-25 認定試験の出題範囲：
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質問 # 91
Which activity is NOT a part of the machine learning life cycle?
- A. Model Deployment
- B. Database Management
- C. Data Access
- D. Modeling
正解：B
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify which activity isn't part of the ML lifecycle.
* Define ML Lifecycle: Includes data access, preparation, modeling, evaluation, deployment, and monitoring.
* Evaluate Options:
* A: Database Management (e.g., DBA tasks) is IT-related, not specific to ML workflows.
* B: Model Deployment (e.g., serving predictions) is a key ML phase-correctly included.
* C: Modeling (e.g., training) is the core of ML-correctly included.
* D: Data Access (e.g., retrieving data) is the first ML step-correctly included.
* Reasoning: Database management supports infrastructure, not the ML process directly.
* Conclusion: A is the outlier.
The OCI Data Science lifecycle includes "data access, exploration, feature engineering, modeling, deployment, and monitoring," per the documentation. Database Management (A) is a general ITtask (e.g., optimizing Oracle DB), not an ML-specific activity, unlike B, C, and D, which are integral to OCI's ML pipeline.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Lifecycle Overview".
質問 # 92
Which cache rules criterion matches if the concatenation of the requested URL path and query are identical to the contents of the value field?
- A. URL_STARTS_WITH
- B. URL_PART_CONTAINS
- C. URL_IS
- D. URL_PART_ENDS_WITH
正解：C
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Match a cache rule criterion for exact URL path and query.
* Understand Cache Rules: Used in OCI (e.g., WAF, CDN) to cache content.
* Evaluate Options:
* A: Contains-Partial match, not exact.
* B: Is-Exact match of full URL (path + query)-correct.
* C: Ends with-Matches end, not full URL.
* D: Starts with-Matches start, not full URL.
* Reasoning: "URL_IS" checks exact equality-fits requirement.
* Conclusion: B is correct.
OCI documentation states: "The URL_IS (B) criterion in cache rules matches when the full URL (path and query) exactly equals the specified value." A, C, and D are partial matches-only B ensures identical concatenation per OCI's caching config.
Oracle Cloud Infrastructure WAF Documentation, "Cache Rules Criteria".
質問 # 93
Which statement is true about origin management in Web Application Firewall (WAF)?
- A. Both the statements are true
- B. Only statement B is true
- C. Multiple origins can be defined
- D. Both the statements are false
- E. Only a single origin can be active for a WAF
- F. Only statement A is true
正解：A
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Determine truth about WAF origin management.
* Understand WAF: Protects apps by routing traffic via origins.
* Evaluate Statements:
* A: Multiple origins-True; WAF supports this.
* B: Single active origin-True; only one is active per policy.
* Evaluate Options:
* C: B only-False; A is true.
* D: Both false-Incorrect.
* E: Both true-Correct per OCI WAF.
* F: A only-False; B is true.
* Conclusion: E is correct.
OCI documentation states: "WAF allows defining multiple origins (A), but only one origin is active per WAF policy at a time (B)-both are true (E)." C, D, and F misalign-E matches OCI's WAF origin management.
Oracle Cloud Infrastructure WAF Documentation, "Origin Management".
質問 # 94
Which OCI Data Science interaction method can function without the need of scripting?
- A. REST APIs
- B. CLI
- C. OCI Console
- D. Language SDKs
正解：C
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI Data Science interaction method that doesn't require scripting.
* Understand Interaction Methods: OCI provides multiple ways to interact with Data Science services- some are GUI-based, others script-based.
* Evaluate Options:
* A. OCI Console: A web-based graphical interface allowing users to manage resources (e.g., create notebook sessions, deploy models) via point-and-click-no scripting needed.
* B. CLI: Command Line Interface requires writing commands (scripts) to execute tasks (e.g., oci data-science notebook-session create).
* C. Language SDKs: Software Development Kits (e.g., Python SDK) require coding to interact programmatically (e.g., oci.data_science.DataScienceClient).
* D. REST APIs: Application Programming Interfaces require scripted HTTP requests (e.g., using curl or a programming language).
* Reasoning: Only the OCI Console (A) offers a no-code, user-friendly interface, while B, C, and D rely on scripting or programming.
* Conclusion: A is the correct answer as it eliminates the need for scripting.
The OCI Console is described in the documentation as "a browser-based interface that allows users to manage OCI Data Science resources, such as creating notebook sessions or jobs, without writing code or scripts." In contrast, the CLI (B) requires command-line scripts, SDKs (C) need programming (e.g., Python), and REST APIs (D) involve scripted API calls. The Console's GUI distinguishes it as the only option functioning without scripting, aligning with Oracle's design for accessibility to non-programmers.
Oracle Cloud Infrastructure Data Science Documentation, "Getting Started with OCI Console" section.
質問 # 95
You want to use ADSTuner to tune the hyperparameters of a supported model you recently trained. You have just started your search and want to reduce the computational cost as well as assess the quality of the model class that you are using. What is the most appropriate search space strategy to choose?
- A. ADSTuner doesn't need a search space to tune the hyperparameters
- B. Pass a dictionary that defines a search space
- C. Detailed
- D. Perfunctory
正解：D
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Select an ADSTuner strategy to minimize cost and assess model quality.
* Understand ADSTuner: Optimizes hyperparameters with configurable search spaces.
* Evaluate Options:
* A: Detailed-Exhaustive, high cost-incorrect.
* B: No search space-False; tuning requires a space.
* C: Perfunctory-Quick, low-cost assessment-correct.
* D: Dictionary-Defines space but not a strategy.
* Reasoning: Perfunctory balances cost and initial quality check.
* Conclusion: C is correct.
OCI documentation states: "ADSTuner's perfunctory strategy (C) performs a quick, low-cost search to assess model quality, ideal for initial tuning." Detailed (A) is costly, B misstates requirements, and D is a method, not a strategy-only C fits the goal.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSTuner Search Strategies".
質問 # 96
Which step is a part of the AutoML pipeline?
- A. Feature Extraction
- B. Model saved to Model Catalog
- C. Model Deployment
- D. Feature Selection
正解：D
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a step in OCI's AutoML pipeline.
* Understand AutoML: Automates model building-includes preprocessing, selection, and tuning.
* Evaluate Options:
* A: Feature Extraction (e.g., PCA) isn't explicitly part of OCI AutoML-too specific.
* B: Saving to Model Catalog is post-AutoML, not a pipeline step.
* C: Deployment is a separate action after AutoML-incorrect.
* D: Feature Selection (e.g., choosing relevant features) is a core AutoML step-correct.
* Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning-D fits.
* Conclusion: D is correct.
OCI AutoML's pipeline includes "feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning," per the documentation. Extraction (A) isn't highlighted, while saving (B) and deployment (C) are post-process actions-only Feature Selection (D) is an integral automated step.
Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".
質問 # 97
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? Pick THREE.
- A. Delete the deployment
- B. Update the deployment to use fewer instances
- C. Reduce the load balancer bandwidth limit so that fewer requests come in
- D. Update the deployment to add more instances
- E. Update the deployment to use a larger virtual machine (more CPUs/memory)
正解：C、D、E
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Prevent deployment failure due to high utilization.
* Evaluate Options:
* A: More instances-Scales capacity-correct.
* B: Delete-Stops service, not a solution.
* C: Fewer instances-Worsens utilization.
* D: Larger VM-Increases resource capacity-correct.
* E: Reduce bandwidth-Limits load-correct.
* Reasoning: A and D boost capacity, E controls demand-proactive fixes.
* Conclusion: A, D, E are correct.
OCI documentation advises: "To handle high utilization, increase instances (A), use a larger compute shape (D), or adjust load balancer bandwidth (E) to manage request volume." B stops service, C reduces capacity- only A, D, E prevent failure per OCI's scaling options.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".
質問 # 98
What is the minimum active storage duration for logs used by Logging Analytics to be archived?
- A. 10 days
- B. 15 days
- C. 60 days
- D. 30 days
正解：D
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Determine minimum log storage duration before archiving in Logging Analytics.
* Understand Logging Analytics: Logs are active before archival.
* Evaluate Options:
* A: 60 days-Too long for minimum.
* B: 10 days-Too short.
* C: 30 days-Standard minimum-correct.
* D: 15 days-Not OCI's default.
* Reasoning: 30 days is OCI's documented minimum active period.
* Conclusion: C is correct.
OCI documentation states: "Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis." B and D are shorter, A is longer-only C matches OCI's policy.
Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".
質問 # 99
Which model has an open-source, open model format that allows you to run machine learning models on different platforms?
- A. TensorFlow
- B. ONNX
- C. PyTorch
- D. PySpark
正解：B
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify an open model format for cross-platform ML model execution.
* Evaluate Options:
* A. PySpark: A big data framework, not a model format.
* B. PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.
* C. TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.
* D. ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.
* Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.
* Conclusion: D is the correct choice.
ONNX (D) is "an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow," per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats-only ONNX ensures cross-platform compatibility.
Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".
質問 # 100
True or false? Data scientists typically need a combination of technical skills, nontechnical ones, and suitable personality traits to be successful.
- A. True
- B. False
正解：A
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Assess required skills for data scientists.
* Analyze Skills:
* Technical: Coding, stats, ML.
* Nontechnical: Communication, business acumen.
* Traits: Curiosity, problem-solving.
* Reasoning: Success requires this mix-e.g., explaining models to stakeholders.
* Conclusion: A (True) is correct.
OCI documentation states: "Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking." This holistic requirement is true (A), not false (B).
Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".
質問 # 101
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?
- A. Oracle Digital Assistant
- B. OCI Vision
- C. OCI Speech
- D. OCI Language
正解：D
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Select an OCI AI service to sort images by language.
* Evaluate Options:
* A: Digital Assistant-Chatbots, not image/language processing.
* B: Vision-Image analysis (e.g., object detection), not language sorting.
* C: Speech-Audio-to-text, not image-based.
* D: Language-Text analysis (e.g., language detection) after OCR-correct.
* Reasoning: Images need OCR (Vision) then language detection (Language)-D fits the sorting task.
* Conclusion: D is correct.
OCI Language "detects and classifies languages in text," often paired with OCI Vision's OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language-Digital Assistant (A) and Speech (C) don't apply. Documentation supports this workflow.
Oracle Cloud Infrastructure Language Documentation, "Language Detection".
質問 # 102
The Oracle AutoML pipeline automates hyperparameter tuning by training the model with different parameters in parallel. You have created an instance of Oracle AutoML as oracle_automl and now you want an output with all the different trials performed by Oracle AutoML. Which of the following commands gives you the results of all trials?
- A. oracle_automl.visualize_adaptive_sampling_trials()
- B. oracle_automl.visualize_tuning_trials()
- C. oracle_automl.print_trials()
- D. oracle_automl.visualize_algorithm_selection_trials()
正解：C
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Get all AutoML trial results.
* Understand AutoML: Trials include hyperparameter tuning outcomes.
* Evaluate Options:
* A: print_trials()-Displays all trial results-correct.
* B: visualize_tuning_trials()-Visualizes tuning, not full list.
* C: visualize_adaptive_sampling_trials()-Specific to sampling, not all trials.
* D: visualize_algorithm_selection_trials()-Specific to algorithms, not all.
* Reasoning: A provides comprehensive trial output.
* Conclusion: A is correct.
OCI AutoML documentation states: "print_trials() outputs a table of all trials performed, including hyperparameters and scores." Visualization methods (B, C, D) focus on specific aspects-only A gives the full list.
Oracle Cloud Infrastructure AutoML Documentation, "Trial Output Methods".
質問 # 103
What does the Data Science Service template in Oracle Resource Manager (ORM) NOTautomatically create?
- A. Required user groups
- B. Individual Data Science users
- C. Policies for a basic use case
- D. Dynamic groups
正解：B
解説：
Detailed Answer in Step-by-Step Solution:
* Understand ORM Template: It automates OCI Data Science setup with predefined configurations.
* Evaluate Components:
* A: User groups are created for role-based access-automated.
* B: Dynamic groups (e.g., for notebook sessions) are included-automated.
* C: Individual users require manual creation via IAM-not automated.
* D: Basic policies (e.g., access to Data Science resources) are included-automated.
* Reasoning: ORM focuses on infrastructure and permissions, not user accounts.
* Conclusion: C is the exception.
The OCI Resource Manager template for Data Science "automatically provisions user groups, dynamic groups, and policies for basic use cases," but "individual users must be created separately in IAM and assigned to groups." C is the only item not handled by the template, per the documentation.
Oracle Cloud Infrastructure Resource Manager Documentation, "Data Science Template".
質問 # 104
Which TWO statements about Oracle Cloud Infrastructure (OCI) Open Data service are true?
- A. Each dataset in Open Data consists of code and tooling usage examples for consumption and reproducibility.
- B. Open Data is a dataset repository made for the people that create, use, and manipulate datasets.
- C. Audio and video formats are not available.
- D. Open Data includes text and image data repositories for AI and ML.
- E. Subscribers can pay and log into Open Data to view curated datasets that are otherwise not available to the public.
- F. A primary goal of Open Data is for users to contribute to the data repositories to expand the content offered.
正解：B、D
解説：
Detailed Answer in Step-by-Step Solution:
* Analyze OCI Open Data: OCI Open Data is a free service providing access to public datasets for AI
/ML use cases.
* Evaluate Statements:
* A: True-Open Data includes text and image datasets (e.g., geospatial images).
* B: False-Video and other formats may be available depending on the dataset; no strict exclusion exists.
* C: False-Datasets may include metadata, but code/tooling examples aren't guaranteed.
* D: True-It's designed for data scientists and analysts who work with datasets.
* E: False-It's not a user-contributed repository; it's curated by Oracle.
* F: False-Open Data is free and public, not subscription-based.
* Select Two: A and D align with the service's purpose and offerings.
OCI Open Data provides access to datasets like text and images (A) for AI/ML, aimed at data professionals (D). It's a free, curated service, not user-contributed (E) or paid (F), and while it focuses on certain formats, it doesn't explicitly exclude audio/video (B). (Reference: Oracle Cloud Infrastructure Open Data Documentation, "Overview of Open Data").
質問 # 105
Which is NOT a valid OCI Data Science notebook session approach?
- A. Run the process directly in the terminal and use Python logging to get updates on the progress of your job
- B. Avoid having multiple users in the same notebook session due to the possibility of resource contention and write conflicts
- C. Ensure you don't execute long-running Python processes in a notebook cell
- D. Authenticate using your notebook session's resource principal to access other OCI resources. Resource principals provide a more secure way to authenticate to resources compared to the OCI configuration and API approach
- E. While connecting to data in OCI Object Storage from your notebook session, the best practice is to make a local copy on the device and then upload it to your notebook session block volume
正解：E
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify an invalid notebook session practice.
* Evaluate Options:
* A: Valid-Long runs should use Jobs, not notebooks.
* B: Valid-Terminal runs with logging are supported.
* C: Valid-Multi-user conflicts are a concern.
* D: Invalid-Copying from Object Storage to block volume is unnecessary; direct access is best.
* E: Valid-Resource principals are secure and recommended.
* Reasoning: D contradicts OCI's direct-access efficiency.
* Conclusion: D is incorrect.
OCI documentation advises: "Access data in Object Storage directly from notebook sessions using SDKs or resource principals-avoid local copies (D) unless necessary." A, B, C, and E are best practices-D is inefficient and not standard.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Best Practices".
質問 # 106
In machine learning, what is the primary difference between supervised and unsupervised learning?
- A. Supervised learning involves data that has been labeled and classified, while unsupervised learning data is unlabeled and unclassified.
- B. Supervised learning is created and managed by the Data Engineer.
- C. Supervised learning is monitored closely by data scientists, while they don't play a role inunsupervised learning.
- D. Supervised learning is only used for image recognition, while unsupervised learning can be used for various analytics applications.
正解：A
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the key difference between supervised and unsupervised learning.
* Define Types:
* Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.
* Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).
* Evaluate Options:
* A: Labeled vs. unlabeled-Core distinction, correct.
* B: Monitoring-Misleading, not the primary difference.
* C: Image recognition-False, supervised applies broadly.
* D: Data Engineer-Irrelevant to learning type.
* Reasoning: A captures the foundational data difference.
* Conclusion: A is correct.
OCI documentation states: "Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns." B, C, and D misrepresent this-only A aligns with OCI's ML definitions and industry standards.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".
質問 # 107
You want to write a program that performs document analysis tasks such as extracting text and tables from a document. Which Oracle AI service would you use?
- A. Oracle Digital Assistant
- B. OCI Language
- C. OCI Speech
- D. OCI Vision
正解：D
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Select an OCI AI service for text and table extraction from documents.
* Evaluate Options:
* A: Language-Text analysis, not extraction-incorrect.
* B: Digital Assistant-Chatbots, not document tasks-incorrect.
* C: Speech-Audio transcription, not documents-incorrect.
* D: Vision-OCR for text/tables-correct.
* Reasoning: Vision's OCR extracts text and tables from document images.
* Conclusion: D is correct.
OCI documentation states: "OCI Vision (D) uses OCR to extract text and tables from documents, supporting document analysis tasks." A analyzes text post-extraction, B and C are unrelated-only D fits per OCI's AI services.
Oracle Cloud Infrastructure Vision Documentation, "Document Analysis Features".
質問 # 108
You want to create an anomaly detection model using the OCI Anomaly Detection service that avoids as many false alarms as possible. False Alarm Probability (FAP) indicates model performance. How would you set the value of the False Alarm Probability?
- A. Zero
- B. Low
- C. High
- D. Use a function
正解：B
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Minimize false alarms in OCI Anomaly Detection.
* Understand FAP: False Alarm Probability-lower FAP means fewer false positives.
* Evaluate Options:
* A: High FAP-Increases false alarms-incorrect.
* B: Low FAP-Reduces false alarms-correct.
* C: Zero FAP-Unrealistic; risks missing true anomalies.
* D: Function-Vague, not a direct setting.
* Reasoning: Low FAP balances sensitivity and false positives- aligns with goal.
* Conclusion: B is correct.
OCI Anomaly Detection documentation states: "Set a low False Alarm Probability (FAP) to minimize false positives, though too low (e.g., zero) may miss anomalies." B fits the goal-high (A) increases errors, zero (C) is impractical, and function (D) isn't specified.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "Configuring FAP".
質問 # 109
You have a dataset with fewer than 1000 observations, and you are using Oracle AutoML to build a classifier.
While visualizing the results of each stage of the Oracle AutoML pipeline, you notice that no visualization has been generated for one of the stages. Which stage is not visualized?
- A. Feature selection
- B. Algorithm selection
- C. Hyperparameter tuning
- D. Adaptive sampling
正解：D
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the non-visualized AutoML stage with small data.
* Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.
* Evaluate Options:
* A: Feature selection-Visualized (e.g., feature importance).
* B: Algorithm selection-Visualized (e.g., algorithm scores).
* C: Adaptive sampling-Skipped/visualization absent for <1000 rows.
* D: Hyperparameter tuning-Visualized (e.g., trial plots).
* Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.
* Conclusion: C is correct.
OCI AutoML documentation notes: "Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it's skipped, and no visualization is generated." Other stages (A, B,D) produce visuals-only C is absent here.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".
質問 # 110
As a data scientist, you use the Oracle Cloud Infrastructure (OCI) Language service to train custommodels.
Which types of custom models can be trained?
- A. Object detection, Text classification
- B. Image classification, Named Entity Recognition (NER)
- C. Text classification, Named Entity Recognition (NER)
- D. Sentiment Analysis, Named Entity Recognition (NER)
正解：C
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify custom model types for OCI Language service.
* Understand OCI Language: Focuses on text analysis, not images.
* Evaluate Options:
* A: Image classification-Incorrect; Language is text-based.
* B: Text classification, NER-Both text tasks-correct.
* C: Sentiment-Pretrained, not custom; NER ok-incorrect.
* D: Object detection-Image-based, incorrect.
* Reasoning: B aligns with OCI Language's custom text capabilities.
* Conclusion: B is correct.
OCI documentation states: "OCI Language supports training custom models for text classification and Named Entity Recognition (NER) (B) using user data." A and D involve images, C includes pretrained sentiment- only B matches OCI Language's custom model scope.
Oracle Cloud Infrastructure Language Documentation, "Custom Model Types".
質問 # 111
What is the primary difference between a data scientist and a data engineer?
- A. A data engineer collects and prepares data, and a data scientist then analyzes it.
- B. A data engineer analyzes data after a data scientist collects and prepares it.
- C. A data engineer builds data pipelines and helps prepare data, while a data scientist is responsible for data collection, preparation, and analysis.
- D. A data engineer creates data flows to be used as templates by the data analyst.
正解：A
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Differentiate data scientist vs. data engineer roles.
* Define Roles:
* Data Engineer: Builds pipelines, prepares data.
* Data Scientist: Analyzes data, builds models.
* Evaluate Options:
* A: Engineer preps, scientist analyzes-Correct division.
* B: Reverses roles-Incorrect.
* C: Overlaps roles-Scientist doesn't typically build pipelines.
* D: Misaligns-Analyst isn't the focus.
* Reasoning: A reflects standard role separation.
* Conclusion: A is correct.
OCI documentation notes: "Data engineers focus on collecting and preparing data through pipelines, while data scientists analyze it to derive insights and build models." A aligns, B inverts, C overcomplicates, and D shifts focus-only A is accurate.
Oracle Cloud Infrastructure Data Science Documentation, "Roles in Data Science".
質問 # 112
Which of these options allow the sharing and loading back of ML models into a notebook session?
- A. Model catalog
- B. Model deployment
- C. Model provenance
- D. Model taxonomy
正解：A
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.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".
質問 # 113
You have created a conda environment in your notebook session. This is the first time you are working with published conda environments. You have also created an Object Storage bucket with permission to manage the bucket. Which TWO commands are required to publish the conda environment?
- A. odsc conda init --bucket_namespace <NAMESPACE> --bucket_name <BUCKET>
- B. odsc conda list --override
- C. odsc conda publish --slug <SLUG>
- D. odsc conda create --file manifest.yaml
- E. conda activate /home/datascience/conda/<SLUG>
正解：A、C
解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Publish a conda env to Object Storage.
* Process: Initialize bucket config, then publish env.
* Evaluate Options:
* A: Publishes env with slug-correct final step.
* B: Lists envs-unrelated to publishing.
* C: Sets bucket details-required setup-correct.
* D: Creates env-not publishing.
* E: Activates env-not for sharing.
* Reasoning: C sets up, A executes-standard workflow.
* Conclusion: A and C are correct.
OCI documentation states: "To publish a conda environment, first run odsc conda init (C) with bucket namespace and name, then odsc conda publish (A) with a slug to upload to Object Storage." B, D, and E serve other purposes-only A and C are required per OCI's process.
Oracle Cloud Infrastructure Data Science CLI Reference, "Publishing Conda Environments".
質問 # 114
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