RELIABLE 1Z0-1110-25 TEST LABS, DUMPS 1Z0-1110-25 GUIDE

Reliable 1z0-1110-25 Test Labs, Dumps 1z0-1110-25 Guide

Reliable 1z0-1110-25 Test Labs, Dumps 1z0-1110-25 Guide

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Oracle 1z0-1110-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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.
Topic 4
  • 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 5
  • 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.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q81-Q86):

NEW QUESTION # 81
Which encryption is used for Oracle Data Science?

  • A. Triple DES (TDES)
  • B. Data Encryption Standard (DES)
  • C. Twofish
  • D. 256-bit Advanced Encryption Standard (AES-256)
  • E. Rivest Shamir Adleman (RSA)

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify encryption standard for OCI Data Science.
* Understand OCI Encryption: Applies to data at rest and in transit.
* Evaluate Options:
* A: AES-256-Industry-standard, OCI default-correct.
* B: DES-Outdated, weak-incorrect.
* C: TDES-Older, less secure-incorrect.
* D: Twofish-Not OCI standard-incorrect.
* E: RSA-Asymmetric, not primary for data at rest-incorrect.
* Reasoning: AES-256 is OCI's go-to for Data Science resources.
* Conclusion: A is correct.
OCI documentation states: "Data Science services encrypt data at rest using AES-256 (A), ensuring high security for notebooks, jobs, and models." B, C, D, and E are either outdated or not used-only A matches OCI's encryption policy.
Oracle Cloud Infrastructure Data Science Documentation, "Data Encryption".


NEW QUESTION # 82
You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?

  • A. The compartment of the Data Flow application
  • B. The local path to your PySpark script
  • C. The bucket used to read/write the PySpark script in Object Storage
  • D. The path to the archive.zip file
  • E. The display name of the application

Answer: A,C,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three required params for an OCI Data Flow app config.
* Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.
* Evaluate Options:
* A: Archive path-Optional if script is in Object Storage-incorrect.
* B: Local script path-Not needed; script is uploaded-incorrect.
* C: Compartment-Required for resource scope-correct.
* D: Bucket-Required for script storage/access-correct.
* E: Display name-Required for app identification-correct.
* Reasoning: C, D, E are mandatory metadata for Data Flow creation-script location is specified via bucket.
* Conclusion: C, D, E are correct.
OCI documentation states: "To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object." Local paths (B) or archives (A) are optional or handled separately-only C, D, E are required per OCI's Data Flow API spec.
Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".


NEW QUESTION # 83
On which option do you set Oracle Cloud Infrastructure Budget?

  • A. Free-form tags
  • B. Compartments
  • C. Instances
  • D. Tenancy

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine where OCI budgets are set.
* Understand Budgets: Track spending across OCI resources.
* Evaluate Options:
* A: Compartments-Scoped within tenancy, not budget root.
* B: Instances-Specific resources, not budget scope.
* C: Tags-Filter costs, not budget setting.
* D: Tenancy-Top-level scope for budgets-correct.
* Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
* Conclusion: D is correct.
OCI documentation states: "Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending." A, B, and C are sub-elements-only D is the primary scope per OCI's cost management.
Oracle Cloud Infrastructure Cost Management Documentation, "Setting Budgets".


NEW QUESTION # 84
In machine learning, what is the primary difference between supervised and unsupervised learning?

  • A. Supervised learning is monitored closely by data scientists, while they don't play a role inunsupervised learning.
  • B. Supervised learning is created and managed by the Data Engineer.
  • C. Supervised learning is only used for image recognition, while unsupervised learning can be used for various analytics applications.
  • D. Supervised learning involves data that has been labeled and classified, while unsupervised learning data is unlabeled and unclassified.

Answer: D

Explanation:
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".


NEW QUESTION # 85
You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage Bucket, train a model, and save it to the model catalog. How would you design the authentication mechanism for the job?

  • A. Store your personal OCI config file and keys in the Vault, and access the Vault through the job run resource principal
  • B. Package your personal OCI config file and keys in the job artifact
  • C. Create a pre-authenticated request (PAR) for the Object Storage bucket and use that in the job code
  • D. Use the resource principal of the job run as the signer in the job code, ensuring there is a dynamic group for this job run with appropriate access to Object Storage and the model catalog

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Securely authenticate a recurring OCI Job.
* Evaluate Options:
* A: PAR-Limited duration, insecure for recurring jobs.
* B: Resource principal-Secure, managed auth for Jobs-correct.
* C: Personal config-Unscalable, security risk.
* D: Vault with personal keys-Complex, still uses user creds.
* Reasoning: B uses OCI's native, secure resource principal mechanism.
* Conclusion: B is correct.
OCI documentation states: "For Jobs accessing sensitive data, use resource principals with a dynamic group (e.
g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog- secure and scalable." A is temporary, C and D risk credential exposure-B is best practice.
Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".


NEW QUESTION # 86
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