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Oracle 1z0-1110-25 시험요강:

주제
소개

주제 1

• Apply MLOps Practices: This domain targets the skills of Cloud Data Scientists and focuses on applying MLOps within the OCI ecosystem. It covers the architecture of OCI MLOps, managing custom jobs, leveraging autoscaling for deployed models, monitoring, logging, and automating ML workflows using pipelines to ensure scalable and production-ready deployments.

주제 2

• OCI Data Science - Introduction & Configuration: This section of the exam measures the skills of Machine Learning Engineers and covers foundational concepts of Oracle Cloud Infrastructure (OCI) Data Science. It includes an overview of the platform, its architecture, and the capabilities offered by the Accelerated Data Science (ADS) SDK. It also addresses the initial configuration of tenancy and workspace setup to begin data science operations in OCI.

주제 3

• Use Related OCI Services: This final section measures the competence of Machine Learning Engineers in utilizing OCI-integrated services to enhance data science capabilities. It includes creating Spark applications through OCI Data Flow, utilizing the OCI Open Data Service, and integrating other tools to optimize data handling and model execution workflows.

주제 4

• Implement End-to-End Machine Learning Lifecycle: This section evaluates the abilities of Machine Learning Engineers and includes an end-to-end walkthrough of the ML lifecycle within OCI. It involves data acquisition from various sources, data preparation, visualization, profiling, model building with open-source libraries, Oracle AutoML, model evaluation, interpretability with global and local explanations, and deployment using the model catalog.

주제 5

• Create and Manage Projects and Notebook Sessions: This part assesses the skills of Cloud Data Scientists and focuses on setting up and managing projects and notebook sessions within OCI Data Science. It also covers managing Conda environments, integrating OCI Vault for credentials, using Git-based repositories for source code control, and organizing your development environment to support streamlined collaboration and reproducibility.

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최신 Oracle Cloud 1z0-1110-25 무료샘플문제 (Q152-Q157):

질문 # 152
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?

• A. Create a new job by setting the required parameters in your code, and create a new job for every code change
• B. Create your code to expect different parameters either as environment variables or as command-line arguments, which are set on every job run with different values
• C. Create a new job every time you need to run your code and pass the parameters as environment variables
• D. Create your code to expect different parameters as command-line arguments, and create a new job every time you run the code

정답：B

설명：
Detailed Answer in Step-by-Step Solution:
* Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs.
* Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args).
* Evaluate Options:
* A: New job per run with env vars-Redundant job creation, inefficient.
* B: New job per run with args-Similarly inefficient due to repeated setup.
* C: Hardcode params, new job per change-Highly inefficient, requires code edits.
* D: Single job, flexible params via env vars or args-Efficient, reusable-correct.
* Reasoning: D minimizes job creation, allows runtime flexibility via configuration-best practice.
* Conclusion: D is correct.
OCI documentation states: "For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK- most efficient approach." Options A, B, and C involve unnecessary job proliferation or code changes-only D aligns with OCI's design for parameterized runs.
Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".

질문 # 153
Which Oracle Cloud Infrastructure (OCI) Data Science policy is invalid?

• A. Allow group DataScienceGroup to use virtual-network-family in compartment DataScience
• B. Allow group DataScienceGroup to use data-science-model-sessions in compartment DataScience
• C. Allow dynamic-group DataScienceDynamicGroup to manage data-science-family in compartment DataScience
• D. Allow dynamic-group DataScienceDynamicGroup to manage data-science-projects in compartment DataScience

정답：B

설명：
Detailed Answer in Step-by-Step Solution:
* Objective: Spot the invalid OCI IAM policy.
* Understand Policies: Use verbs (e.g., use, manage) and resource types.
* Evaluate Options:
* A: Valid-virtual-network-family is a resource type.
* B: Invalid-No data-science-model-sessions resource type; likely meant data-science-models.
* C: Valid-data-science-projects is correct.
* D: Valid-data-science-family aggregates Data Science resources.
* Reasoning: B uses a nonexistent resource type-error in syntax.
* Conclusion: B is incorrect.
OCI IAM documentation lists valid resource types: "data-science-models, data-science-projects, data-science- family-but not data-science-model-sessions (B)." A, C, D are correct-B is invalid due to this typo
/misunderstanding.
Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".

질문 # 154
Using Oracle AutoML, you are tuning hyperparameters on a supported model class and have specified a time budget. AutoML terminates computation once the time budget is exhausted. What would you expect AutoML to return in case the time budget is exhausted before hyperparameter tuning is completed?

• A. The last generated hyperparameter configuration
• B. The current best-known hyperparameter configuration
• C. A random hyperparameter configuration
• D. A hyperparameter configuration with a minimum learning rate

정답：B

설명：
Detailed Answer in Step-by-Step Solution:
* Objective: Predict AutoML's output when time runs out during tuning.
* Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.
* Evaluate Options:
* A: Best-known config-Logical, reflects optimization goal-correct.
* B: Last config-Ignores prior better results-incorrect.
* C: Minimum learning rate-Arbitrary, not performance-based.
* D: Random-Defeats tuning purpose.
* Reasoning: AutoML prioritizes the best config found within the budget.
* Conclusion: A is correct.
OCI AutoML documentation states: "If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics." Last (B), minimum (C), or random (D) configs aren't selected-only A aligns with OCI's optimization strategy.
Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".

질문 # 155
You have trained a binary classifier for a loan application and saved this model into the model catalog. A colleague wants to examine the model, and you need to share the model with your colleague. From the model catalog, which model artifacts can be shared?

• A. Metadata, hyperparameters, metrics only
• B. Model metadata and hyperparameters only
• C. Models, model metadata, hyperparameters, metrics
• D. Models and metrics only

정답：C

설명：
Detailed Answer in Step-by-Step Solution:
* Understand Model Catalog: The OCI Model Catalog stores trained models and associated details.
* Identify Shareable Artifacts: When sharing, all components-model file, metadata (e.g., name, description), hyperparameters (e.g., learning rate), and metrics (e.g., accuracy)-are accessible.
* Evaluate Options:
* A: Excludes the model itself-incorrect.
* B: Excludes metrics-incorrect.
* C: Excludes metadata and hyperparameters-incorrect.
* D: Includes all components-correct.
* Conclusion: D is comprehensive and accurate.
The OCI Model Catalog allows sharing of the model artifact (the trained model), metadata, hyperparameters, and performance metrics, enabling full examination by colleagues. This isdetailed in the official documentation. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Overview").

질문 # 156
Which of the following analytical and statistical techniques do data scientists commonly use?

• A. All of the above
• B. Regression
• C. Clustering
• D. Classification

정답：A

설명：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify common data science techniques.
* Define Techniques:
* Classification: Predicts categories (e.g., spam vs. not).
* Regression: Predicts continuous values (e.g., sales).
* Clustering: Groups data (e.g., customer segments).
* Evaluate Options:
* A, B, C: All are standard ML/statistical methods.
* D: Encompasses all-correct as they're widely used.
* Reasoning: These are foundational in data science workflows.
* Conclusion: D is correct.
OCI documentation lists "classification, regression, and clustering as core techniques in data science, supported by tools like ADS SDK and AutoML." All (D) are common per OCI's ML framework, not just subsets (A, B, C).
Oracle Cloud Infrastructure Data Science Documentation, "Analytical Techniques".

질문 # 157
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