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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
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	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	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 4	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 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 (Q56-Q61):

NEW QUESTION # 56
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor data. What are you trying to determine? How does the required False Alarm Probability setting affect an anomaly detection model?

A. It adds a score to each signal indicating the probability that it's a false alarm
B. It changes the sensitivity of the model to detecting anomalies
C. It determines how many false alarms occur before an error message is generated
D. It is used to disable the reporting of false alarms

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Understand FAP's effect in OCI Anomaly Detection.
* Evaluate Options:
* A: Disable reporting-Incorrect; FAP sets threshold.
* B: Sensitivity-Correct; lower FAP reduces false positives.
* C: Error message-Incorrect; not a count mechanism.
* D: Score per signal-Incorrect; FAP is a global setting.
* Reasoning: FAP adjusts detection threshold-key to sensitivity.
* Conclusion: B is correct.
OCI documentation states: "False Alarm Probability (FAP) controls the model's sensitivity-lower values reduce false positives, higher values increase detection." B aligns-others misrepresent FAP's role.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Configuration".

NEW QUESTION # 57
Which statement about Oracle Cloud Infrastructure Multi-Factor Authentication (MFA) is NOT valid?

A. Users cannot disable MFA for themselves
B. An administrator can disable MFA for another user
C. Users must install a supported authenticator app on the mobile device they intend to register for MFA
D. A user can register only one device to use for MFA

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the invalid MFA statement.
* Evaluate Options:
* A: True-Users can't disable MFA; admin-controlled.
* B: False-Multiple devices can be registered-invalid.
* C: True-Authenticator app is required.
* D: True-Admins can disable MFA.
* Reasoning: B contradicts OCI's multi-device support.
* Conclusion: B is incorrect.
OCI documentation states: "Users can register multiple devices for MFA (B is false), must use an authenticator app (C), and cannot disable MFA themselves (A)-admins can (D)." Only B is not valid per OCI's IAM MFA policy.
Oracle Cloud Infrastructure IAM Documentation, "Multi-Factor Authentication".

NEW QUESTION # 58
As a data scientist, you use the Oracle Cloud Infrastructure (OCI) Language service to train custommodels.
Which types of custom models can be trained?

A. Text classification, Named Entity Recognition (NER)
B. Sentiment Analysis, Named Entity Recognition (NER)
C. Image classification, Named Entity Recognition (NER)
D. Object detection, Text classification

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify custom model types for OCI Language service.
* Understand OCI Language: Focuses on text analysis, not images.
* Evaluate Options:
* A: Image classification-Incorrect; Language is text-based.
* B: Text classification, NER-Both text tasks-correct.
* C: Sentiment-Pretrained, not custom; NER ok-incorrect.
* D: Object detection-Image-based, incorrect.
* Reasoning: B aligns with OCI Language's custom text capabilities.
* Conclusion: B is correct.
OCI documentation states: "OCI Language supports training custom models for text classification and Named Entity Recognition (NER) (B) using user data." A and D involve images, C includes pretrained sentiment- only B matches OCI Language's custom model scope.
Oracle Cloud Infrastructure Language Documentation, "Custom Model Types".

NEW QUESTION # 59
True or false? Bias is a common problem in data science applications.

A. False
B. True

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Assess if bias is a common issue in data science.
* Define Bias: Systematic errors in data/models (e.g., skewed training data).
* Evaluate Statement:
* Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws-widely recognized in ML.
* Examples: Gender bias in hiring models, racial bias in facial recognition.
* Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.
* Conclusion: A (True) is correct.
OCI documentation notes: "Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks." This aligns with industry standards-bias is a well-documented issue, making A true.
Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".

NEW QUESTION # 60
Which of these is a unique feature of the published conda environment?

A. Provides a comprehensive environment to solve business use cases
B. Allows you to save the conda environment in a block volume
C. Provides availability on network session reactivation
D. Allows you to save the conda environment to an Object Storage Bucket

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand Published Conda Environments: In OCI Data Science, these are custom conda environments shared across users via Object Storage.
* Evaluate Options:
* A: Vague-All conda environments can address use cases; not unique to "published."
* B: Incorrect-Availability on reactivation applies to session persistence, not publishing.
* C: Correct-Publishing saves the environment to Object Storage for sharing/reuse.
* D: Incorrect-Block volumes store session data, not published environments.
* Reasoning: The unique aspect of "published" environments is their storage in Object Storage (via odsc conda publish), enabling team access.
* Conclusion: C is the distinctive feature.
The OCI Data Science documentation highlights that "published conda environments are saved to an OCI Object Storage Bucket, allowing them to be shared across notebook sessions and users." This distinguishes C from A (generic), B (session-related), and D (block volume is for session state, not publishing). Publishing to Object Storage is the defining trait per Oracle's design.
Oracle Cloud Infrastructure Data Science Documentation, "Managing Conda Environments - Publishing" section.

NEW QUESTION # 61
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Oracle - Fantastic 1z0-1110-25 - Updated Oracle Cloud Infrastructure 2025 Data Science Professional Testkings

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q56-Q61):

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