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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q44-Q49):

NEW QUESTION # 44
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?

A. Low
B. Zero
C. Use a function
D. High

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.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "Configuring FAP".

NEW QUESTION # 45
You want to make your model more parsimonious to reduce the cost of collecting and processing data. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method would be appropriate to display the correlation between Continuous and Categorical features?

A. cramersv_plot()
B. correlation_ratio_plot()
C. pearson_plot()
D. corr()

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Visualize correlation between continuous and categorical features using ADS SDK.
* Understand Correlation Types:
* Continuous vs. Continuous: Pearson correlation.
* Categorical vs. Categorical: Cramer's V.
* Continuous vs. Categorical: Correlation ratio (eta).
* Evaluate Options:
* A. corr(): General correlation (Pearson), not suited for mixed types-incorrect.
* B. correlation_ratio_plot(): Plots correlation ratio for continuous-categorical-correct.
* C. pearson_plot(): Not an ADS method; Pearson is continuous-only-incorrect.
* D. cramersv_plot(): Cramer's V for categorical-categorical-incorrect.
* Reasoning: Correlation ratio measures association between continuous and categorical variables-ideal for heatmap in this mixed scenario.
* Conclusion: B is correct.
OCI documentation states: "The correlation_ratio_plot() method (B) in ADS SDK generates a heatmap displaying the correlation ratio between continuous and categorical features, suitable for identifying highly correlated features for removal." corr() (A) defaults to Pearson, pearson_plot() (C) isn't real, and cramersv_plot() (D) is for categorical pairs-only B aligns with OCI's ADS capabilities for this use case.
Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization Methods".

NEW QUESTION # 46
In which two ways can you improve data durability in Oracle Cloud Infrastructure Object Storage?

A. Enable Versioning
B. Setup volumes in a RAID1 configuration
C. Enable server-side encryption
D. Limit delete permissions
E. Enable client-side encryption

Answer: A,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two methods to enhance Object Storage durability.
* Understand Durability: Ensures data isn't lost-focus on redundancy and protection.
* Evaluate Options:
* A: RAID1-Block volume feature, not Object Storage.
* B: Encryption-Secures data, not durability.
* C: Versioning-Retains old versions, prevents loss-correct.
* D: Limit delete-Prevents accidental deletion-correct.
* E: Client encryption-Secures, not durability-focused.
* Reasoning: C and D directly protect against data loss-durability-focused.
* Conclusion: C and D are correct.
OCI documentation states: "Improve Object Storage durability with Versioning (C) to retain previous object versions and by limiting delete permissions (D) to prevent accidental loss." A isn't applicable, B and E focus on security-only C and D enhance durability per OCI's storage features.
Oracle Cloud Infrastructure Object Storage Documentation, "Data Durability Options".

NEW QUESTION # 47
Which statement about resource principals is true?

A. A resource principal is a feature of IAM that enables resources to be authorized principal actors.
B. The Data Science service does not provide authentication via a notebook session's or job run's resource principal to access other OCI resources.
C. When you authenticate using a resource principal, you need to create and manage credentials to access OCI resources.
D. A resource principal is not a secure way to authenticate to resources, compared to the OCI configuration and API key approach.

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.
Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".

NEW QUESTION # 48
You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your network configuration?

A. FastConnect to an on-premises network
B. The NAT Gateway with public internet access
C. Service Gateway with private subnet access
D. Primary Virtual Network Interface Card (VNIC)

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Diagnose a network timeout during pip install in a notebook session.
* Understand Notebook Networking: Sessions run in a VCN; internet access requires specific configs.
* Analyze Timeout: Indicates failure to reach the public PyPI repository-likely no internet outbound route.
* Evaluate Options:
* A: NAT Gateway-Provides internet access for private subnets-correct fix.
* B: Service Gateway-Accesses OCI services privately, not public internet.
* C: FastConnect-Links to on-premises, not public internet.
* D: VNIC-Essential but present by default; doesn't solve internet access.
* Reasoning: NAT Gateway enables outbound traffic to public repos like PyPI.
* Conclusion: A is correct.
OCI documentation notes: "Notebook sessions in a private subnet require a NAT Gateway to access public internet resources, such as PyPI, for package installation via pip. Without it, network timeouts occur." Service Gateway (B) is for OCI services, FastConnect (C) is irrelevant, and VNIC (D) is standard-only A resolves the issue.
Oracle Cloud Infrastructure Data Science Documentation, "Networking for Notebook Sessions".

NEW QUESTION # 49
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