FIRST-HAND ORACLE 1Z0-1110-25 LATEST TEST QUESTION: ORACLE CLOUD INFRASTRUCTURE 2025 DATA SCIENCE PROFESSIONAL

First-hand Oracle 1z0-1110-25 Latest Test Question: Oracle Cloud Infrastructure 2025 Data Science Professional

First-hand Oracle 1z0-1110-25 Latest Test Question: Oracle Cloud Infrastructure 2025 Data Science Professional

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Oracle 1z0-1110-25 Exam Syllabus Topics:

TopicDetails
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
  • 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 4
  • 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 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 (Q95-Q100):

NEW QUESTION # 95
You are a data scientist leveraging the Oracle Cloud Infrastructure (OCI) Language AI service for various types of text analyses. Which TWO capabilities can you utilize with this tool?

  • A. Topic classification
  • B. Punctuation correction
  • C. Sentence diagramming
  • D. Table extraction
  • E. Sentiment analysis

Answer: A,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two OCI Language AI capabilities.
* Understand OCI Language: Focuses on text analysis tasks.
* Evaluate Options:
* A: Table extraction-Vision, not Language-incorrect.
* B: Punctuation correction-Not offered-incorrect.
* C: Sentence diagramming-Not supported-incorrect.
* D: Topic classification-Supported (custom/pretrained)-correct.
* E: Sentiment analysis-Supported (pretrained)-correct.
* Reasoning: D and E are core text analysis features of OCI Language.
* Conclusion: D and E are correct.
OCI documentation states: "OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features." A belongs to Vision, B and C aren't available-only D and E match OCI Language's capabilities.
Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".


NEW QUESTION # 96
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 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 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 # 97
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 # 98
Which statement accurately describes an aspect of machine learning models?

  • A. Static predictions become increasingly accurate over time.
  • B. A high-quality model will not need to be retrained as new information is received.
  • C. Model performance degrades over time due to changes in data.
  • D. Data models are more static and generally require fewer updates than software code.

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find a true statement about ML models.
* Evaluate Options:
* A: True-Data drift (changes in data distribution) degrades performance over time.
* B: False-Static predictions don't improve without retraining.
* C: False-Models need updates as data changes, unlike static software.
* D: False-Even high-quality models require retraining with new data.
* Reasoning: A reflects the reality of data drift, a common ML challenge.
* Conclusion: A is correct.
OCI documentation notes: "Model performance can degrade over time due to data drift, where the underlying data distribution changes, necessitating monitoring and retraining." B, C, and D contradict this-static predictions don't improve (B), models aren't static (C), and retraining is needed (D). A is the accurate aspect.
Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Drift".


NEW QUESTION # 99
Which Oracle Data Safe feature minimizes the amount of personal data and allows internal test, development, and analytics teams to operate with reduced risk?

  • A. Data masking
  • B. Data auditing
  • C. Security assessment
  • D. Data encryption
  • E. Data discovery

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the Data Safe feature that reduces personal data exposure.
* Understand Data Safe: Secures sensitive data in OCI databases.
* Evaluate Options:
* A: Encryption-Protects data, doesn't minimize it.
* B: Assessment-Identifies risks, doesn't alter data.
* C: Masking-Obfuscates personal data (e.g., SSNs)-correct.
* D: Discovery-Locates sensitive data, doesn't reduce it.
* E: Auditing-Tracks access, doesn't minimize data.
* Reasoning: Masking replaces sensitive data, reducing risk for teams-fits goal.
* Conclusion: C is correct.
OCI documentation states: "Data masking (C) in Data Safe transforms sensitive data into anonymized versions, minimizing exposure for test, dev, and analytics use." A protects, B assesses, D finds, E audits- only C reduces data per OCI's Data Safe features.
Oracle Cloud Infrastructure Data Safe Documentation, "Data Masking Overview".


NEW QUESTION # 100
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