1z0-1110-25 Exam Dumps - Oracle Cloud Infrastructure Questions and Answers

Detailed Answer in Step-by-Step Solution:

Objective: Identify the firewall type protecting against web app attacks like SQL injection and XSS.

Understand Firewall Types:

Stateful Inspection: Tracks connection states, not app-specific.

Web Application Firewall (WAF): Targets web app vulnerabilities.

Incident Firewall: Not a recognized term.

Packet Filtering: Basic packet rules, not app-aware.

Evaluate Options:

A: Stateful—General network, not web-specific—incorrect.

B: WAF—Designed for SQLi, XSS—correct.

C: Incident—Non-existent—incorrect.

D: Packet—Low-level, not app-focused—incorrect.

Reasoning: WAF specializes in web app security—matches requirement.

Conclusion: B is correct.

OCI documentation states: “Web Application Firewall (WAF) (B) protects against web application attacks like SQL injection and cross-site scripting by inspecting HTTP traffic.” A and D handle network-level threats, C isn’t real—only B aligns with OCI’s WAF purpose.

Oracle Cloud Infrastructure WAF Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Define LIME’s core concept.

Understand LIME: Explains individual predictions with local surrogate models.

Evaluate Options:

A: Complex global, simple local—Correct LIME principle.

B: Agnosticism—True but not the key idea.

C: Global/local similarity—False.

D: Local vs. global agnosticism—Incorrect distinction.

Reasoning: A captures LIME’s local approximation focus.

Conclusion: A is correct.

OCI documentation notes: “LIME (A) explains predictions by approximating complex global models with simpler local surrogate models around specific instances.” B, C, and D misalign—only A reflects LIME’s foundational idea per OCI’s interpretability tools.

Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".

Detailed Answer in Step-by-Step Solution:

Objective: Identify correct statements about installing Python libraries in OCI Data Science.

Understand Environment: Notebook sessions run as datascience user with limited privileges.

Evaluate Options:

A: False—Yum isn’t available; pip is the primary tool.

B: True—Custom repos work with proper network config.

C: False—No root access; managed environment.

D: True—PyPI packages installable with internet (NAT Gateway).

E: False—Youcaninstall beyond preinstalled; likely meant opposite.

Reasoning: B and D are true; E’s intent seems reversed (common exam error)—corrected to B, D.

Conclusion: B, D (assuming E typo).

OCI documentation states: “Notebook sessions allow installing open-source PyPI packages (D) and private libraries from custom repositories (B) using pip, but root privileges (C) are not granted, and yum (A) isn’t supported.” E contradicts capability—corrected, B and D are accurate.

Oracle Cloud Infrastructure Data Science Documentation, "Installing Python Libraries".

Detailed Answer in Step-by-Step Solution:

Objective: Run a complex Python project as an OCI Job.

Evaluate Options:

A: Auto-identification—False; entrypoint must be set.

B: Rewrite—Unnecessary, inefficient.

C: Auto-executable—False; needs explicit entrypoint.

D: ZIP with entrypoint—Correct, flexible approach.

Reasoning: D preserves structure, specifies execution.

Conclusion: D is correct.

OCI documentation states: “For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py).” Auto-detection (A, C) isn’t supported, and B discards structure—D is best.

Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".

Detailed Answer in Step-by-Step Solution:

Objective: Get summary info from an ADSDataset object.

Evaluate Options:

A: Correlation matrix—Specific, not full summary.

B: Converts to XGBoost—Not for summary.

C: Executes computation—Not summary-focused.

D: Displays summary (types, counts, dist)—correct.

Reasoning: show_in_notebook() provides a comprehensive overview.

Conclusion: D is correct.

OCI documentation states: “show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook.” A is partial, B and C are unrelated—only D meets the need per ADS SDK.

Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".

Detailed Answer in Step-by-Step Solution:

Objective: Select an auth method for third-party APIs lacking OCI signature support.

Understand OCI Auth: Typically uses API keys, but alternatives exist for non-standard APIs.

Evaluate Options:

A: Username/password—Not API-friendly, insecure.

B: API Signing Key—Requires signature-based auth, unsupported here.

C: SSH Key—For instance access, not APIs.

D: Auth Token—Simple token for API calls—correct.

Reasoning: Auth Token provides a bearer token for APIs without signature complexity.

Conclusion: D is correct.

OCI documentation states: “For third-party APIs not supporting signature-based authentication, use an Auth Token (D), a secure, revocable token for accessing OCI resources via REST APIs.” A, B, and C don’t fit non-signature scenarios—only D ensures compatibility per OCI’s IAM options.

Oracle Cloud Infrastructure IAM Documentation, "Auth Tokens for API Access".

Detailed Answer in Step-by-Step Solution:

Understand OCI Data Labeling Capabilities: OCI Data Labeling is designed to annotate data for machine learning, supporting specific file types like images, text documents, and videos.

Evaluate Options:

A. Video footage: Supported for tasks like object detection or action recognition.

B. Images: Supported for image classification, object detection, etc.

C. Typewritten document: Supported as text data for tasks like entity extraction or classification.

D. Purchase orders: While potentially text-based, this is ambiguous without format clarification (e.g., PDF, image). OCI supports text annotation, but “purchase orders” isn’t a specific file type—it’s assumed as text here.

E. Audio recording: Not supported, as OCI Data Labeling focuses on visual and textual data, not audio.

Select Three: A (video), B (images), and C (text documents) are explicitly supported file types.

OCI Data Labeling supports annotating datasets of images, text, and videos, as per the official documentation. Video footage (A) can be annotated for tasks like object tracking, images (B) for classification or detection, and typewritten documents (C) for text-based annotations (e.g., named entity recognition). Audio files (E) are not supported, and while purchase orders (D) could be text, the question specifies “typewritten document” as a clearer match. (Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Supported Data Types").

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".

Detailed Answer in Step-by-Step Solution:

Objective: Identify supported annotation classes in OCI Data Labeling.

Understand Tool: Supports image/text annotations for ML.

Evaluate Options:

A: Object detection—Yes (bounding boxes).

B: Named entity—Text-specific, not primary for images.

C: Classification—Yes (labels for images/text).

D: Key-point—Not listed in OCI docs.

E: Polygonal—Not explicitly supported.

F: Semantic segmentation—Yes (pixel-level).

Reasoning: A, C, F match OCI’s image/text focus.

Conclusion: A, C, F are correct.

OCI Data Labeling supports “object detection (A), classification (C), and semantic segmentation (F) for images and text,” per documentation. B is text-specific, D and E aren’t highlighted—only A, C, F are core classes.

Oracle Cloud Infrastructure Data Labeling Documentation, "Annotation Types".

Detailed Answer in Step-by-Step Solution:

Objective: Identify common data science techniques.

Define Techniques:

Classification: Predicts categories (e.g., spam vs. not).

Regression: Predicts continuous values (e.g., sales).

Clustering: Groups data (e.g., customer segments).

Evaluate Options:

A, B, C: All are standard ML/statistical methods.

D: Encompasses all—correct as they’re widely used.

Reasoning: These are foundational in data science workflows.

Conclusion: D is correct.

OCI documentation lists “classification, regression, and clustering as core techniques in data science, supported by tools like ADS SDK and AutoML.” All (D) are common per OCI’s ML framework, not just subsets (A, B, C).

Oracle Cloud Infrastructure Data Science Documentation, "Analytical Techniques".