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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 5	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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q140-Q145):

NEW QUESTION # 140
Which type of file system does File Storage use?

A. iSCSI
B. Paravirtualized
C. NFSv3
D. NVMe SSD

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the file system type for OCI File Storage.
* Understand File Storage: Network-attached storage in OCI.
* Evaluate Options:
* A: NFSv3-Network File System, used by File Storage-correct.
* B: iSCSI-Block storage protocol, not File Storage.
* C: Paravirtualized-Virtualization mode, not file system.
* D: NVMe SSD-Hardware, not file system.
* Reasoning: NFSv3 is OCI File Storage's protocol.
* Conclusion: A is correct.
OCI documentation states: "File Storage uses NFSv3 (A) as its file system protocol, providing shared storage across instances." B, C, and D are unrelated-only A aligns with OCI's File Storage design.
Oracle Cloud Infrastructure File Storage Documentation, "File System Protocol".

NEW QUESTION # 141
In which two ways can you improve data durability in Oracle Cloud Infrastructure Object Storage?

A. Setup volumes in a RAID1 configuration
B. Enable server-side encryption
C. Enable client-side encryption
D. Limit delete permissions
E. Enable Versioning

Answer: D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two methods to enhance Object Storage durability.
* Understand Durability: Ensures data isn't lost-focus on redundancy and protection.
* Evaluate Options:
* A: RAID1-Block volume feature, not Object Storage.
* B: Encryption-Secures data, not durability.
* C: Versioning-Retains old versions, prevents loss-correct.
* D: Limit delete-Prevents accidental deletion-correct.
* E: Client encryption-Secures, not durability-focused.
* Reasoning: C and D directly protect against data loss-durability-focused.
* Conclusion: C and D are correct.
OCI documentation states: "Improve Object Storage durability with Versioning (C) to retain previous object versions and by limiting delete permissions (D) to prevent accidental loss." A isn't applicable, B and E focus on security-only C and D enhance durability per OCI's storage features.
Oracle Cloud Infrastructure Object Storage Documentation, "Data Durability Options".

NEW QUESTION # 142
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. COCO
B. Data Labeling Service Proprietary JSON
C. Spacy
D. CONLL V2003

Answer: A,B

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 # 143
As a data scientist, you are trying to automate a machine learning (ML) workflow and have decided to use Oracle Cloud Infrastructure (OCI) AutoML Pipeline. Which THREE are part of the AutoML Pipeline?

A. Feature Selection
B. Adaptive Sampling
C. Model Deployment
D. Feature Extraction
E. Algorithm Selection

Answer: A,B,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three stages in OCI AutoML Pipeline.
* Understand Pipeline: Automates ML steps from data to model training.
* Evaluate Options:
* A: Feature Selection-Selects relevant features-correct.
* B: Adaptive Sampling-Reduces data size-correct.
* C: Model Deployment-Post-pipeline step-incorrect.
* D: Feature Extraction-Not explicit in OCI AutoML-incorrect.
* E: Algorithm Selection-Chooses best model-correct.
* Reasoning: A, B, E are core automated stages; C and D are separate.
* Conclusion: A, B, E are correct.
OCI documentation lists "AutoML Pipeline stages as adaptive sampling (B), feature selection (A), algorithm selection (E), and hyperparameter tuning." Deployment (C) is post-pipeline, and extraction (D) isn't highlighted-only A, B, E are included per OCI's design.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".

NEW QUESTION # 144
You have an embarrassingly parallel or distributed batch job with a large amount of data running using Data Science Jobs. What would be the best approach to run the workload?

A. Create a job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
B. Create a new job for every job run that you have to run in parallel, because the Data Science Job service can have only one job per job
C. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel
D. Create the job in Data Science Jobs and start a job run. When it is done, start a new job run until you achieve the number of runs required

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize an embarrassingly parallel job in OCI Data Science Jobs.
* Define Embarrassingly Parallel: Tasks are independent, ideal for simultaneous runs.
* Evaluate Options:
* A: Multiple simultaneous runs-Leverages parallelism-correct.
* B: One job per run-Misstates capability; unnecessary complexity.
* C: Sequential runs-Inefficient, ignores parallelism.
* D: False-Jobs support parallelism.
* Reasoning: A maximizes efficiency for parallel tasks.
* Conclusion: A is correct.
OCI documentation states: "For embarrassingly parallel workloads, create a single Job and launch multiple simultaneous Job Runs to process data in parallel." B misinterprets limits, C wastes time, and D denies capability-only A fits OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Runs".

NEW QUESTION # 145
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