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| Section | Objectives |
|---|---|
| OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions |
| Implement End-to-End Machine Learning Lifecycle | - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Deploy models and consume model endpoints - Prepare and manage datasets |
| Use Related OCI Services | - Design machine learning solutions for business use cases - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing |
| Apply MLOps Practices | - Monitor and maintain machine learning models - Implement model lifecycle management - Use best practices for operationalizing ML solutions |
| Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects |
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NEW QUESTION # 54
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 # 55
Which components are a part of the OCI Identity and Access Management service?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify IAM components in OCI.
Understand IAM: Manages access via policies, groups, etc.
Evaluate Options:
A: Policies—Core IAM component—correct.
B: Subnets—Networking, not IAM.
C: Instances—Compute, not IAM.
D: VCN—Networking, not IAM.
Reasoning: Only A is an IAM element—others are resources.
Conclusion: A is correct.
OCI documentation states: “IAM includes components like policies (A), groups, and compartments to control resource access.” B, C, and D are infrastructure, not IAM—only A fits per OCI’s IAM framework.
1: Oracle Cloud Infrastructure IAM Documentation, "IAM Components".
NEW QUESTION # 56
You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Optimize embarrassingly parallel workload in OCI Jobs.
Evaluate Options:
A: Sequential runs—Inefficient for parallel tasks.
B: Simultaneous runs—Maximizes parallelism—correct.
C: False—Jobs support parallelism.
D: One job per run—Misstates capability, wasteful.
Reasoning: B leverages OCI’s parallel run support.
Conclusion: B is correct.
OCI documentation states: “For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently.” A is slow, C is incorrect, and Dovercomplicates—B is the best approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".
NEW QUESTION # 57
Which OCI Data Science interaction method can function without the need of scripting?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI Data Science interaction method that doesn’t require scripting.
Understand Interaction Methods: OCI provides multiple ways to interact with Data Science services—some are GUI-based, others script-based.
Evaluate Options:
A . OCI Console: A web-based graphical interface allowing users to manage resources (e.g., create notebook sessions, deploy models) via point-and-click—no scripting needed.
B . CLI: Command Line Interface requires writing commands (scripts) to execute tasks (e.g., oci data-science notebook-session create).
C . Language SDKs: Software Development Kits (e.g., Python SDK) require coding to interact programmatically (e.g., oci.data_science.DataScienceClient).
D . REST APIs: Application Programming Interfaces require scripted HTTP requests (e.g., using curl or a programming language).
Reasoning: Only the OCI Console (A) offers a no-code, user-friendly interface, while B, C, and D rely on scripting or programming.
Conclusion: A is the correct answer as it eliminates the need for scripting.
The OCI Console is described in the documentation as “a browser-based interface that allows users to manage OCI Data Science resources, such as creating notebook sessions or jobs, without writing code or scripts.” In contrast, the CLI (B) requires command-line scripts, SDKs (C) need programming (e.g., Python), and REST APIs (D) involve scripted API calls. The Console’s GUI distinguishes it as the only option functioning without scripting, aligning with Oracle’s design for accessibility to non-programmers.
1: Oracle Cloud Infrastructure Data Science Documentation, "Getting Started with OCI Console" section.
NEW QUESTION # 58
Which statement about resource principals is true?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.
Evaluate Options:
A: False—Resource principals eliminate manual credential management.
B: False—They’re secure, leveraging IAM policies, not less secure than API keys.
C: False—Data Science supports resource principals for accessing resources (e.g., Object Storage).
D: True—Resource principals are an IAM feature authorizing resources as actors.
Reasoning: D captures the essence of resource principals as an IAM mechanism.
Conclusion: D is correct.
OCI documentation states: “A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies.” This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.
1: Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".
NEW QUESTION # 59
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