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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

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

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 3

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 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

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 (Q39-Q44):

NEW QUESTION # 39
Which components are a part of the OCI Identity and Access Management service?

A. Policies
B. Compute instances
C. VCN
D. Regional subnets

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify IAM components in OCI.
* Understand IAM: Manages access via policies, groups, etc.
* Evaluate Options:
* A: Policies-Core IAM component-correct.
* B: Subnets-Networking, not IAM.
* C: Instances-Compute, not IAM.
* D: VCN-Networking, not IAM.
* Reasoning: Only A is an IAM element-others are resources.
* Conclusion: A is correct.
OCI documentation states: "IAM includes components like policies (A), groups, and compartments to control resource access." B, C, and D are infrastructure, not IAM-only A fits per OCI's IAM framework.
Oracle Cloud Infrastructure IAM Documentation, "IAM Components".

NEW QUESTION # 40
You are building a model and need input that represents data as morning, afternoon, or evening. However, the data contains a timestamp. What part of the Data Science lifecycle would you be in when creating the new variable?

A. Model type selection
B. Feature engineering
C. Model validation
D. Data access

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate the lifecycle stage for transforming timestamps into categories.
* Understand Lifecycle:
* Model Type Selection: Choosing algorithms-post-data prep.
* Model Validation: Evaluating performance-post-training.
* Data Access: Retrieving raw data-pre-transformation.
* Feature Engineering: Creating new features-correct.
* Evaluate Options:
* A: Too late-incorrect.
* B: Post-model-incorrect.
* C: Pre-transformation-incorrect.
* D: Feature creation-correct.
* Reasoning: Converting timestamps to time periods is feature engineering.
* Conclusion: D is correct.
OCI documentation states: "Feature engineering (D) transforms raw data, such as timestamps into categorical variables (e.g., morning/afternoon), to enhance model inputs." A, B, and C occur at different stages-only D fits OCI's lifecycle for this task.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Stage".

NEW QUESTION # 41
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which of the following TWO prevailing metrics would you use for evaluating a multiclass classification model?

A. F1 Score
B. R-Squared
C. Recall
D. Mean squared error
E. Explained variance score

Answer: A,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select two metrics for multiclass classification in AutoML.
* Understand Multiclass Metrics: Focus on class-specific performance-classification, not regression.
* Evaluate Options:
* A. Recall: Measures true positives per class-key for multiclass-correct.
* B. Mean squared error: Regression metric-incorrect.
* C. F1 Score: Balances precision and recall-standard for multiclass-correct.
* D. R-Squared: Regression fit-incorrect.
* E. Explained variance: Regression metric-incorrect.
* Reasoning: A and C assess classification accuracy across multiple classes-fit AutoML's evaluation.
* Conclusion: A and C are correct.
OCI AutoML documentation states: "For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance." B, D, and E are regression- focused-only A and C are supported and relevant per OCI's AutoML metrics suite.
Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".

NEW QUESTION # 42
After you have created and opened a notebook session, you want to use the Accelerated Data Science (ADS) SDK to access your data and get started with exploratory data analysis. From which TWO places can you access the ADS SDK?

A. Python Package Index (PyPI)
B. Conda environment in OCI Data Science
C. Oracle Autonomous Data Warehouse
D. Oracle Machine Learning
E. Oracle Big Data Service

Answer: A,B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate sources for ADS SDK in OCI.
* Understand ADS SDK: A Python library for Data Science tasks (e.g., EDA).
* Evaluate Options:
* A: Big Data Service-Spark-focused, not ADS source.
* B: Machine Learning-Separate service, not ADS-related.
* C: Conda in OCI Data Science-Preinstalled ADS in notebook sessions.
* D: PyPI-Public source to install ADS (pip install oracle-ads).
* E: ADW-Database, not an SDK source.
* Reasoning: C (preinstalled) and D (installable) are practical access points.
* Conclusion: C and D are correct.
OCI documentation states: "The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle-ads." Big Data (A), Machine Learning (B), and ADW (E) don't host ADS-only C and D apply.
Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".

NEW QUESTION # 43
You have been given a collection of digital files required for a business audit. They consist of several different formats that you would like to annotate using Oracle Cloud Infrastructure (OCI) Data Labeling.
Which THREE types of files could this tool annotate?

A. An audio recording of a phone conversation
B. Images of computer server racks
C. A typewritten document that details an annual budget
D. Video footage of a conversation in a conference room
E. A collection of purchase orders for office supplies

Answer: B,C,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Labeling Capabilities: OCI Data Labeling is designed to annotate data for machine learning, supporting specific file types like images, text documents, and videos.
* Evaluate Options:
* A. Video footage: Supported for tasks like object detection or action recognition.
* B. Images: Supported for image classification, object detection, etc.
* C. Typewritten document: Supported as text data for tasks like entity extraction or classification.
* D. Purchase orders: While potentially text-based, this is ambiguous without format clarification (e.g., PDF, image). OCI supports text annotation, but "purchase orders" isn't a specific file type- it's assumed as text here.
* E. Audio recording: Not supported, as OCI Data Labeling focuses on visual and textual data, not audio.
* Select Three: A (video), B (images), and C (text documents) are explicitly supported file types.
OCI Data Labeling supports annotating datasets of images, text, and videos, as per the official documentation.
Video footage (A) can be annotated for tasks like object tracking, images (B) for classification or detection, and typewritten documents (C) for text-based annotations (e.g., named entity recognition). Audio files (E) are not supported, and while purchase orders (D) could be text, the question specifies "typewritten document" as a clearer match. (Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Supported Data Types").

NEW QUESTION # 44
......

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