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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	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	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	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	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 (Q40-Q45):

NEW QUESTION # 40
Which statement about Oracle Cloud Infrastructure Data Science Jobs is true?

A. You must use a single Shell/Bash or Python artifact to run a job
B. Jobs provisions the infrastructure to run a process on-demand
C. You must create and manage your own Jobs infrastructure
D. Jobs comes with a set of standard tasks that cannot be customized

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a true statement about OCI Data Science Jobs.
* Understand OCI Jobs: Jobs automate ML tasks (e.g., training) on managed infrastructure.
* Evaluate Options:
* A: True-Jobs provision OCI compute resources on-demand for task execution.
* B: False-Users define custom tasks (e.g., Python scripts), not limited to standard ones.
* C: False-Infrastructure is fully managed by OCI, not user-managed.
* D: False-Multiple artifacts (e.g., ZIP with dependencies) can be used, not just one file.
* Reasoning: A reflects OCI's managed, on-demand provisioning model for Jobs.
* Conclusion: A is correct.
The OCI Data Science documentation states: "Jobs provision compute infrastructure on-demand to execute user-defined tasks, such as model training or data processing, on fully managed OCI resources." B is incorrect (customization is allowed), C contradicts the managed nature, and D misstates artifact flexibility-only A accurately describes Jobs.
Oracle Cloud Infrastructure Data Science Documentation, "Jobs Overview".

NEW QUESTION # 41
Which activity is NOT a part of the machine learning life cycle?

A. Database Management
B. Modeling
C. Data Access
D. Model Deployment

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify which activity isn't part of the ML lifecycle.
* Define ML Lifecycle: Includes data access, preparation, modeling, evaluation, deployment, and monitoring.
* Evaluate Options:
* A: Database Management (e.g., DBA tasks) is IT-related, not specific to ML workflows.
* B: Model Deployment (e.g., serving predictions) is a key ML phase-correctly included.
* C: Modeling (e.g., training) is the core of ML-correctly included.
* D: Data Access (e.g., retrieving data) is the first ML step-correctly included.
* Reasoning: Database management supports infrastructure, not the ML process directly.
* Conclusion: A is the outlier.
The OCI Data Science lifecycle includes "data access, exploration, feature engineering, modeling, deployment, and monitoring," per the documentation. Database Management (A) is a general ITtask (e.g., optimizing Oracle DB), not an ML-specific activity, unlike B, C, and D, which are integral to OCI's ML pipeline.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Lifecycle Overview".

NEW QUESTION # 42
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. model_artifact_validate.py
B. runtime.yaml
C. requirements.txt
D. score.py

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 # 43
In machine learning, what is the primary difference between supervised and unsupervised learning?

A. Supervised learning is only used for image recognition, while unsupervised learning can be used for various analytics applications.
B. Supervised learning is created and managed by the Data Engineer.
C. Supervised learning is monitored closely by data scientists, while they don't play a role inunsupervised learning.
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 # 44
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?

A. Create your code to expect different parameters either as environment variables or as command-line arguments, which are set on every job run with different values
B. Create your code to expect different parameters as command-line arguments, and create a new job every time you run the code
C. Create a new job every time you need to run your code and pass the parameters as environment variables
D. Create a new job by setting the required parameters in your code, and create a new job for every code change

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs.
* Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args).
* Evaluate Options:
* A: New job per run with env vars-Redundant job creation, inefficient.
* B: New job per run with args-Similarly inefficient due to repeated setup.
* C: Hardcode params, new job per change-Highly inefficient, requires code edits.
* D: Single job, flexible params via env vars or args-Efficient, reusable-correct.
* Reasoning: D minimizes job creation, allows runtime flexibility via configuration-best practice.
* Conclusion: D is correct.
OCI documentation states: "For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK- most efficient approach." Options A, B, and C involve unnecessary job proliferation or code changes-only D aligns with OCI's design for parameterized runs.
Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".

NEW QUESTION # 45
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