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[General] Latest Oracle 1z0-1110-25 Exam Duration - 1z0-1110-25 Study Guides

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【General】 Latest Oracle 1z0-1110-25 Exam Duration - 1z0-1110-25 Study Guides

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The Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) certification examination is an essential component of professional development, and passing this Oracle 1z0-1110-25 test can increase career options and a rise in salary. Nonetheless, getting ready for the Prepare for your 1z0-1110-25 Exam may be difficult, and many working professionals have trouble locating the 1z0-1110-25 practice questions they need to succeed in this endeavor.
Oracle 1z0-1110-25 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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.
Topic 2
  • 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.
Topic 3
  • 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.
Topic 4
  • 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.
Topic 5
  • 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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q94-Q99):NEW QUESTION # 94
Arrange the following in the correct Git Repository workflow order:
* Install, configure, and authenticate Git.
* Configure SSH keys for the Git repository.
* Create a local and remote Git repository.
* Commit files to the local Git repository.
* Push the commit to the remote Git repository.
  • A. 2, 3, 1, 4, 5
  • B. 4, 2, 3, 1, 5
  • C. 1, 2, 3, 4, 5
  • D. 3, 5, 1, 2, 4
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Step 1: Install, configure, and authenticate Git: Git must be installed and configured (e.g., git config -- global user.name) before any repository actions.
* Step 2: Configure SSH keys: SSH keys are set up (e.g., ssh-keygen) and added to the remote service (e.
g., GitHub, OCI Code Repository) for secure access.
* Step 3: Create local and remote Git repository: Initialize a local repo (git init) and create/link a remote repo (e.g., git remote add origin).
* Step 4: Commit files: Add files (git add .) and commit them locally (git commit -m "message").
* Step 5: Push to remote: Push local commits to the remote repo (git push origin main).
* Evaluate Options: Only D (1, 2, 3, 4, 5) follows this logical sequence; others (e.g., A starts with SSH before Git installation) are illogical.
The standard Git workflow in OCI Data Science or general practice begins with installing Git (1), configuring SSH for secure access (2), creating repositories (3), committing locally (4), and pushing remotely (5). The OCI Code Repository documentation aligns with this: "First, install Git and configure authentication (e.g., SSH), then set up repositories and manage code." D is the only option reflecting this industry-standard process.
Oracle Cloud Infrastructure Code Repository Documentation, "Git Workflow Basics".

NEW QUESTION # 95
You are working as a data scientist for a healthcare company. They decide to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended stepsto develop a PySpark application in Oracle Cloud Infrastructure (OCI) Data Science?
  • A. Install a Spark conda environment, configure core-site.xml, launch a notebook session, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application
  • B. Configure core-site.xml, install a PySpark conda environment, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application, launch a notebook session
  • C. Launch a notebook session, install a PySpark conda environment, configure core-site.xml, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
  • D. Launch a notebook session, configure core-site.xml, install a PySpark conda environment, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence steps for PySpark app development.
* Steps:
* Launch notebook: First-sets up environment.
* Install PySpark conda: Second-adds Spark libraries.
* Configure core-site.xml: Third-connects to data.
* Develop app: Fourth-writes code.
* Data Flow: Fifth-scales (optional).
* Evaluate: D (1, 2, 3, 4, 5) matches this logical order.
* Reasoning: Notebook first, then setup and coding.
* Conclusion: D is correct.
OCI documentation states: "1) Launch a notebook session, 2) install a PySpark conda env, 3) configure core- site.xml, 4) develop your PySpark app, 5) optionally use Data Flow (D)." Other orders (A, B, C) misplace notebook launch or config-D is correct.
Oracle Cloud Infrastructure Data Science Documentation, "PySpark Development".

NEW QUESTION # 96
You have created a model and want to use Accelerated Data Science (ADS) SDK to deploy the model. Where are the artifacts to deploy this model with ADS?
  • A. Data Science Artifactory
  • B. Model Depository
  • C. Model Catalog
  • D. OCI Vault
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate artifacts for ADS model deployment.
* Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI.
* Evaluate Options:
* A: Vault-Stores secrets, not models.
* B: Depository-Not an OCI term.
* C: Model Catalog-Stores models/artifacts for deployment-correct.
* D: Artifactory-Not an OCI service.
* Reasoning: Model Catalog is OCI's model repository for ADS.
* Conclusion: C is correct.
OCI documentation states: "ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored." Vault (A) is for secrets, B and D aren't real-only C supports ADS deployment.
Oracle Cloud Infrastructure Data Science Documentation, "ADS Model Deployment".

NEW QUESTION # 97
Which OCI cloud service lets you centrally manage the encryption keys that protect your data and the secret credentials that you use to securely access resources?
  • A. Vault
  • B. Data Guard
  • C. Cloud Guard
  • D. Data Safe
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for key and secret management.
* Evaluate Options:
* A: Data Safe-Database security, not key management.
* B: Cloud Guard-Threat detection, not keys.
* C: Data Guard-DB replication, not keys.
* D: Vault-Key and secret management-correct.
* Reasoning: Vault is OCI's dedicated service for crypto keys and secrets.
* Conclusion: D is correct.
OCI documentation states: "OCI Vault (D) centrally manages encryption keys and secrets, securing data and resource access." A, B, and C serve other purposes-only D matches per OCI's securityservices.
Oracle Cloud Infrastructure Vault Documentation, "Overview".

NEW QUESTION # 98
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 reduces the amount of data storage space that's required for data science applications.
  • C. It enables them to use a representative subset of data to build accurate analytical models more quickly.
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine the primary benefit of data sampling.
* Define Sampling: Selecting a subset of data to represent the whole-used in ML/statistics.
* Evaluate Options:
* A: Small batches reduce resources-True but not the main purpose.
* B: Reduces storage-Incidental, not the goal.
* C: Representative subset for faster, accurate models-Core purpose of sampling.
* Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).
* Conclusion: C is correct.
OCI documentation states: "Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical." A focuses on resources (secondary), B on storage (not primary)-only C captures the analytical intent per OCI's AutoML sampling approach.
Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".

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