100% Pass Quiz Oracle - 1z0-1110-25 - Professional Reliable Oracle Cloud Infrastructure 2025 Data Science Professional Exam Tips

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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	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 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	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.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q37-Q42):

NEW QUESTION # 37
Which Oracle Cloud Service provides restricted access to target resources?

A. Bastion
B. Internet Gateway
C. Load Balancer
D. SSL Certificate

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for restricted resource access.
* Evaluate Options:
* A: Bastion-Secure, temporary access to resources-correct.
* B: Internet Gateway-Public access, not restricted.
* C: Load Balancer-Distributes traffic, not access control.
* D: SSL Certificate-Secures comms, not access.
* Reasoning: Bastion limits access (e.g., SSH) to specific targets.
* Conclusion: A is correct.
OCI documentation states: "OCI Bastion (A) provides restricted, audited access to target resources like instances, typically via SSH." B, C, and D don't restrict-only A fits per OCI's security services.
Oracle Cloud Infrastructure Bastion Documentation, "Overview".

NEW QUESTION # 38
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor data. How does the required False Alarm Probability setting affect an anomaly detection model?

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

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Understand the effect of False Alarm Probability (FAP) in OCI Anomaly Detection.
* Understand FAP: Controls false positive rate-threshold for anomaly flagging.
* Evaluate Options:
* A: Disable reporting-Incorrect; FAP sets sensitivity, not on/off.
* B: Changes sensitivity-Correct; lower FAP = fewer false positives-correct.
* C: Count-based error-Incorrect; not a counter.
* D: Score per signal-Incorrect; FAP is a global setting.
* Reasoning: FAP adjusts detection threshold-direct impact on sensitivity.
* Conclusion: B is correct.
OCI documentation states: "False Alarm Probability (FAP) (B) adjusts the model's sensitivity in Anomaly Detection-lower values increase specificity, reducing false positives." A, C, and D misinterpret FAP's role- only B aligns with OCI's anomaly detection tuning.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Settings".

NEW QUESTION # 39
Which is NOT a part of Observability and Management Services?

A. Logging
B. Logging Analytics
C. Event Services
D. OCI Management Service

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the non-Observability and Management (O&M) service in OCI.
* Understand O&M: Includes monitoring, logging, events tools.
* Evaluate Options:
* A: Event Services-Triggers actions, part of O&M-correct.
* B: OCI Management Service-Not a defined O&M service-incorrect.
* C: Logging Analytics-Log analysis, O&M component-correct.
* D: Logging-Log collection, O&M component-correct.
* Reasoning: B isn't listed in OCI's O&M suite-others are.
* Conclusion: B is correct (not part of O&M).
OCI documentation lists "Observability and Management Services as including Event Services (A), Logging Analytics (C), and Logging (D)-'OCI Management Service' (B) is not a recognized component." B appears to be a misnomer-only A, C, D are O&M per OCI's service catalog.
Oracle Cloud Infrastructure Observability and Management Documentation, "Service Overview".

NEW QUESTION # 40
You have trained a binary classifier for a loan application and saved this model into the model catalog. A colleague wants to examine the model, and you need to share the model with your colleague. From the model catalog, which model artifacts can be shared?

A. Models and metrics only
B. Models, model metadata, hyperparameters, metrics
C. Model metadata and hyperparameters only
D. Metadata, hyperparameters, metrics only

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand Model Catalog: The OCI Model Catalog stores trained models and associated details.
* Identify Shareable Artifacts: When sharing, all components-model file, metadata (e.g., name, description), hyperparameters (e.g., learning rate), and metrics (e.g., accuracy)-are accessible.
* Evaluate Options:
* A: Excludes the model itself-incorrect.
* B: Excludes metrics-incorrect.
* C: Excludes metadata and hyperparameters-incorrect.
* D: Includes all components-correct.
* Conclusion: D is comprehensive and accurate.
The OCI Model Catalog allows sharing of the model artifact (the trained model), metadata, hyperparameters, and performance metrics, enabling full examination by colleagues. This isdetailed in the official documentation. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Overview").

NEW QUESTION # 41
You are working as a data scientist for a healthcare company. They decide to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended stepsto develop a PySpark application in Oracle Cloud Infrastructure (OCI) Data Science?

A. Launch a notebook session, configure core-site.xml, install a PySpark conda environment, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
B. Launch a notebook session, install a PySpark conda environment, configure core-site.xml, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
C. Install a Spark conda environment, configure core-site.xml, launch a notebook session, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application
D. Configure core-site.xml, install a PySpark conda environment, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application, launch a notebook session

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence steps for PySpark app development.
* Steps:
* Launch notebook: First-sets up environment.
* Install PySpark conda: Second-adds Spark libraries.
* Configure core-site.xml: Third-connects to data.
* Develop app: Fourth-writes code.
* Data Flow: Fifth-scales (optional).
* Evaluate: D (1, 2, 3, 4, 5) matches this logical order.
* Reasoning: Notebook first, then setup and coding.
* Conclusion: D is correct.
OCI documentation states: "1) Launch a notebook session, 2) install a PySpark conda env, 3) configure core- site.xml, 4) develop your PySpark app, 5) optionally use Data Flow (D)." Other orders (A, B, C) misplace notebook launch or config-D is correct.
Oracle Cloud Infrastructure Data Science Documentation, "PySpark Development".

NEW QUESTION # 42
......

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100% Pass Quiz Oracle - 1z0-1110-25 - Professional Reliable Oracle Cloud Infrastructure 2025 Data Science Professional Exam Tips

Oracle 1z0-1110-25 Valid Test Notes & 1z0-1110-25 Exam Training

Oracle 1z0-1110-25 Exam Syllabus Topics:

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q37-Q42):

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