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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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	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 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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q13-Q18):

NEW QUESTION # 13
You are working as a Data Scientist for a healthcare company. You have a series of neurophysiological data on OCI Data Science and have developed a convolutional neural network (CNN) classification model. It predicts the source of seizures in drug-resistant epileptic patients. You created a model artifact with all the necessary files. When you deployed the model, it failed to run because you did not point to the correct conda environment in the model artifact. Where would you provide instructions to use the correct conda environment?

A. score.py
B. runtime.yaml
C. model_artifact_validate.py
D. requirements.txt

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine where to specify the conda environment for an OCI model deployment.
* Understand Model Deployment: Requires artifacts like score.py and runtime.yaml to define runtime settings.
* Evaluate Options:
* A. score.py: Contains inference logic (e.g., load_model(), predict())-not for environment specs.
* B. runtime.yaml: Defines deployment runtime, including conda environment path-correct.
* C. requirements.txt: Lists pip dependencies-not used in OCI for conda environments.
* D. model_artifact_validate.py: Not a standard artifact; doesn't exist in OCI deployment.
* Reasoning: runtime.yaml specifies the conda env (e.g., slug: pyspark30_p37_cpu_v2)-failure to set this causes deployment errors.
* Conclusion: B is correct.
OCI documentation states: "The runtime.yaml file in a model artifact specifies the runtime environment, including the conda environment path (e.g., ENVIRONMENT_SLUG: pyspark30_p37_cpu_v2), ensuring the deployed model uses the correct dependencies." score.py (A) handles inference, requirements.txt (C) is for pip (not conda in OCI), and D isn't valid-only B addresses the conda issue per OCI's deployment process.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".

NEW QUESTION # 14
Which of the following best describes the principal goal of data science?

A. To collect and archive exhaustive datasets from various source systems for corporate record-keeping uses.
B. To collect and prepare data for use as part of analytics applications.
C. Data science is focused on output of the analysis.
D. To mine and analyze large amounts of data in order to uncover information that can be used for operational improvements and business gains.

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define data science's main goal.
* Evaluate Options:
* A: Archiving-Not the focus; too narrow.
* B: Analyze for insights/business value-Core purpose-correct.
* C: Prep for analytics-Means, not the end goal.
* D: Output-focused-Vague, incomplete.
* Reasoning: B captures the actionable insight generation central to data science.
* Conclusion: B is correct.
OCI documentation defines data science as "mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value." A is storage-focused, C is preparatory, and D is unclear-only B reflects the principal goal per OCI's mission.
Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".

NEW QUESTION # 15
While working with Git on Oracle Cloud Infrastructure (OCI) Data Science, you notice that two of the operations are taking more time than the others due to your slow internet speed. Which TWO operations would experience the delay?

A. Updating the local repo to match the content from a remote repository
B. Making a commit that is taking a snapshot of the local repository for the next push
C. Pushing changes to a remote repository
D. Moving the changes into staging area for the next commit
E. Converting an existing local project folder to a Git repository

Answer: A,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Analyze Git Operations: Identify which depend on internet speed.
* Evaluate Options:
* A. Staging (git add): Local operation-adds files to the index; no network involved.
* B. Updating local repo (git pull): Downloads remote changes-requires internet, slowed by poor connectivity.
* C. Pushing changes (git push): Uploads local commits to remote-network-dependent, delayed by slow speed.
* D. Committing (git commit): Local snapshot-no network needed.
* E. Converting to Git repo (git init): Local initialization-no internet required.
* Reasoning: Only B and C involve network transfers, directly impacted by slow internet.
* Conclusion: B and C are the correct choices.
Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as "bandwidth-sensitive" in OCI's guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user's machine, unaffected by internet speed. This matches standard Git behavior and OCI's implementation.
Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".

NEW QUESTION # 16
You are attempting to save a model from a notebook session to the model catalog by using the Accelerated Data Science (ADS) SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which two should you look for to ensure permissions are set up correctly?

A. The policy for a dynamic group grants manage permissions for the model catalog in this compartment
B. The networking configuration allows access to Oracle Cloud Infrastructure services through a Service Gateway
C. The policy for your user group grants manage permissions for the model catalog in this compartment
D. A dynamic group has rules that match the notebook sessions in its compartment
E. The model artifact is saved to the block volume of the notebook session

Answer: A,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Troubleshoot a 404 auth error when saving a model with resource principal.
* Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies-no user credentials.
* Analyze 404 Error: Indicates permission failure-likely IAM misconfiguration.
* Evaluate Options:
* A: Block volume storage-Irrelevant to auth; it's about saving locally-incorrect.
* B: Dynamic group matching-Ensures notebook is recognized-correct.
* C: User group policy-Not used with resource principal-incorrect.
* D: Dynamic group policy-Grants catalog access-correct.
* E: Service Gateway-Network-related, not auth-specific-incorrect.
* Reasoning: Resource principal needs B (group inclusion) and D (policy perms)-404 points to these.
* Conclusion: B and D are correct.
OCI documentation states: "For ADS SDK to save to the Model Catalog using resource principal, ensure (1) a dynamic group includes notebook sessions with matching rules (e.g., resource.type
='datasciencenotebooksession') (B), and (2) a policy grants manage data-science-models to that dynamic group (D)." A is storage, C is user-based, E is network-only B and D fix the auth issue per OCI's IAM setup.
Oracle Cloud Infrastructure Data Science Documentation, "Resource Principal with Model Catalog".

NEW QUESTION # 17
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. Images of computer server racks
B. Video footage of a conversation in a conference room
C. A collection of purchase orders for office supplies
D. An audio recording of a phone conversation
E. A typewritten document that details an annual budget

Answer: A,B,E

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 # 18
......

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RELIABLE 1Z0-1110-25 TEST PREP, 1Z0-1110-25 PDF EXAM DUMP

Reliable 1z0-1110-25 Test Prep, 1z0-1110-25 Pdf Exam Dump