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| Section | Objectives |
|---|---|
| OCI Data Science - Introduction and Configuration | - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources |
| Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Implement model lifecycle management - Monitor and maintain machine learning models |
| Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects - Manage notebook sessions and compute resources |
| Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases - Integrate OCI Data and AI services |
| Implement End-to-End Machine Learning Lifecycle | - Build, train, and evaluate machine learning models - Prepare and manage datasets - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Deploy models and consume model endpoints |
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NEW QUESTION # 114
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets from reference libraries and index websites such as scikit-learn?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify ADS class for dataset access (e.g., scikit-learn).
Evaluate Options:
A: DataLabeling—Not an ADS class.
B: DatasetBrowser—Not real.
C: SecretKeeper—Credentials, not data.
D: DatasetFactory—Loads datasets (e.g., open())—correct.
Reasoning: DatasetFactory simplifies library dataset access.
Conclusion: D is correct.
OCI documentation states: “DatasetFactory (D) in ADS SDK accesses datasets from libraries like scikit-learn (e.g., DatasetFactory.open('sklearn.datasets:load_iris')).” A, B, and C don’t exist or apply—only D fits.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".
NEW QUESTION # 115
What do you use the score.py file for?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine the purpose of score.py in OCI Data Science model deployment.
Understand Model Deployment: When deploying a model in OCI, artifacts include score.py, runtime.yaml, etc.
Evaluate Options:
A: Infrastructure configuration (e.g., compute shape) is handled by deployment settings, not score.py.
B: score.py contains the inference logic (e.g., load_model(), predict())—correct.
C: Conda environment is defined in runtime.yaml or a requirements file—not score.py.
D: Scaling (e.g., instance count) is set in deployment configuration—not score.py.
Reasoning: score.py is the script executed by the deployment endpoint to load the model and make predictions.
Conclusion: B is the correct purpose.
The OCI Data Science documentation states: “The score.py file is a required artifact for model deployment, containing the inference logic—functions like load_model() to load the model and predict() to generate predictions from input data.” Infrastructure (A) and scaling (D) are managed via the OCI Console or SDK, while the environment (C) is specified in runtime.yaml. B is the precise role of score.py in OCI’s deployment workflow.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - score.py".
NEW QUESTION # 116
How can you collaborate with team members in OCI Data Science Workspace?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).
Evaluate Options:
A: Access control—Possible but not primary collaboration.
B: Version control (e.g., Git)—Standard for code sharing—correct.
C: Shared instance—Not supported; sessions are single-user.
D: Chat/video—Not a feature of OCI Data Science.
Reasoning: B leverages Git for team collaboration—OCI’s recommended method.
Conclusion: B is correct.
OCI documentation states: “Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks.” A is limited, C isn’t feasible, and D isn’t available—only B matches OCI’s collaboration approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Collaboration with Git".
NEW QUESTION # 117
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor dat a. How does the required False Alarm Probability setting affect an anomaly detection model?
Answer: B
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.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Settings".
NEW QUESTION # 118
You want to create an anomaly detection model using the OCI Anomaly Detection service that avoids as many false alarms as possible. False Alarm Probability (FAP) indicates model performance. How would you set the value of the False Alarm Probability?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Minimize false alarms in OCI Anomaly Detection.
Understand FAP: False Alarm Probability—lower FAP means fewer false positives.
Evaluate Options:
A: High FAP—Increases false alarms—incorrect.
B: Low FAP—Reduces false alarms—correct.
C: Zero FAP—Unrealistic; risks missing true anomalies.
D: Function—Vague, not a direct setting.
Reasoning: Low FAP balances sensitivity and false positives— aligns with goal.
Conclusion: B is correct.
OCI Anomaly Detection documentation states: “Set a low False Alarm Probability (FAP) to minimize false positives, though too low (e.g., zero) may miss anomalies.” B fits the goal—high (A) increases errors, zero (C) is impractical, and function (D) isn’t specified.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "Configuring FAP".
NEW QUESTION # 119
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