1Z0-1110-25 EXAM DURATION, 1Z0-1110-25 VALID TORRENT

1z0-1110-25 Exam Duration, 1z0-1110-25 Valid Torrent

1z0-1110-25 Exam Duration, 1z0-1110-25 Valid Torrent

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Oracle 1z0-1110-25 Exam Syllabus Topics:

TopicDetails
Topic 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.
Topic 2
  • 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 3
  • 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 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
  • 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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q79-Q84):

NEW QUESTION # 79
Which two statements are true about published conda environments?

  • A. Your notebook session acts as the source to share published conda environments with team members
  • B. In addition to service job run environment variables, conda environment variables can be used inData Science Jobs
  • C. They are curated by Oracle Cloud Infrastructure (OCI) Data Science
  • D. The odsc conda init command is used to configure the location of published conda environments
  • E. You can only create a published conda environment by modifying a Data Science conda environment

Answer: B,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify true statements about published conda environments in OCI.
* Understand Published Conda: Custom envs shared via Object Storage.
* Evaluate Options:
* A: False-Users create them, not OCI-curated.
* B: True-odsc conda init sets up access to published envs.
* C: False-Object Storage, not notebook, is the source.
* D: False-Can create from scratch, not just modifications.
* E: True-Conda env vars extend job configs.
* Reasoning: B configures access, E enhances flexibility-match OCI behavior.
* Conclusion: B and E are correct.
OCI documentation states: "odsc conda init (B) configures the bucket for published conda environments, and Jobs can use conda environment variables (E) alongside service vars." A misattributes curation, C misplaces source, and D limits creation-only B and E are true per OCI's conda management.
Oracle Cloud Infrastructure Data Science Documentation, "Published Conda Environments".


NEW QUESTION # 80
What does the Data Science Service template in Oracle Resource Manager (ORM) NOTautomatically create?

  • A. Policies for a basic use case
  • B. Required user groups
  • C. Dynamic groups
  • D. Individual Data Science users

Answer: D

Explanation:
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".


NEW QUESTION # 81
Which statement accurately describes an aspect of machine learning models?

  • A. Static predictions become increasingly accurate over time.
  • B. Data models are more static and generally require fewer updates than software code.
  • C. A high-quality model will not need to be retrained as new information is received.
  • D. Model performance degrades over time due to changes in data.

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find a true statement about ML models.
* Evaluate Options:
* A: True-Data drift (changes in data distribution) degrades performance over time.
* B: False-Static predictions don't improve without retraining.
* C: False-Models need updates as data changes, unlike static software.
* D: False-Even high-quality models require retraining with new data.
* Reasoning: A reflects the reality of data drift, a common ML challenge.
* Conclusion: A is correct.
OCI documentation notes: "Model performance can degrade over time due to data drift, where the underlying data distribution changes, necessitating monitoring and retraining." B, C, and D contradict this-static predictions don't improve (B), models aren't static (C), and retraining is needed (D). A is the accurate aspect.
Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Drift".


NEW QUESTION # 82
You are given the task of writing a program that sorts document images by language. Which Oracle service would you use?

  • A. OCI Vision
  • B. OCI Speech
  • C. Oracle Digital Assistant
  • D. OCI Language

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the Oracle service to sort document images by language.
* Task Breakdown: Requires extracting text from images (OCR) and detecting language-two potential services involved.
* Evaluate Options:
* A. Oracle Digital Assistant: Builds chatbots-irrelevant to image or language processing.
* B. OCI Language: Detects and classifies languages in text-ideal for sorting after text extraction.
* C. OCI Speech: Transcribes audio to text-not applicable to images.
* D. OCI Vision: Performs OCR to extract text from images-necessary but not sufficient for language sorting.
* Reasoning: The task emphasizes "sorting by language." OCI Vision extracts text, but OCI Language identifies the language (e.g., English, Spanish). Since the question asks for one service and focuses on sorting, OCI Language (B) is the best fit, assuming text extraction is a precursor step.
* Conclusion: B is correct.
OCI Language "provides language detection and classification capabilities, enabling identification of languages in text extracted from documents," per the documentation. OCI Vision handles OCR, but the sorting task aligns with OCI Language (B). Digital Assistant (A) and Speech (C) don't apply, and while Vision (D) is a prerequisite, B is the primary service for language sorting as per OCI's AI service design.
Oracle Cloud Infrastructure Language Documentation, "Language Detection Features".


NEW QUESTION # 83
You have just completed analyzing a set of images by using Oracle Cloud Infrastructure (OCI) Data Labeling, and you want to export the annotated data. Which TWO formats are supported?

  • A. Spacy
  • B. Data Labeling Service Proprietary JSON
  • C. CONLL V2003
  • D. COCO

Answer: B,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify export formats for OCI Data Labeling.
* Understand Export: Annotated data is exported for ML use.
* Evaluate Options:
* A: CONLL V2003-Text-specific, not OCI-supported.
* B: COCO-Standard for image annotations-correct.
* C: Proprietary JSON-OCI's default format-correct.
* D: Spacy-NLP format, not supported.
* Reasoning: B and C are image-compatible per OCI docs.
* Conclusion: B and C are correct.
OCI documentation states: "Data Labeling exports annotations in COCO format (B) for image tasks and a proprietary JSON format (C) specific to the service." CONLL (A) and Spacy (D) are text/NLP-focused-not supported for OCI's image annotations.
Oracle Cloud Infrastructure Data Labeling Documentation, "Export Formats".


NEW QUESTION # 84
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