Important Oracle 1Z0-1110-25 Exam Questions

Answer: A, C, F

Explanation:

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'.

Answer: C

Explanation:

Detailed Answer in Step-by-Step Solution:

Objective: Find the stage where data imbalance (e.g., skewed classes) is identified.

Understand Stages:

Data Modeling: Training models---assumes data is prepared.

Data Monitoring: Post-deployment tracking---not for initial analysis.

Data Exploration: Analyzing data properties (e.g., distributions)---key for imbalance.

Data Access: Retrieving data---no analysis yet.

Evaluate Options:

A: Modeling uses data, doesn't detect imbalance---incorrect.

B: Monitoring tracks performance, not initial data issues---incorrect.

C: Exploration (e.g., via pandas) reveals imbalances---correct.

D: Access is just retrieval---incorrect.

Reasoning: Imbalance is assessed during exploration (e.g., class counts).

Conclusion: C is correct.

OCI documentation notes: ''Data Exploration involves analyzing the dataset to understand its characteristics, such as identifying class imbalances or missing values, using tools like ADS SDK or Jupyter notebooks.'' Modeling (A) and Monitoring (B) occur later, while Access (D) is pre-analysis---only Exploration (C) fits this role.

: Oracle Cloud Infrastructure Data Science Documentation, 'Data Exploration Stage'.

Answer: D

Explanation:

Detailed Answer in Step-by-Step Solution:

Objective: Identify the function that measures the difference between predicted and actual values in machine learning.

Understand ML Functions:

Optimizer function: Adjusts model parameters to minimize error (e.g., gradient descent)---it uses the cost, not defines it.

Fit function: Trains the model by fitting it to data---process-oriented, not a measure.

Update function: Typically updates weights during training---not a standard term for error measurement.

Cost function: Quantifies prediction error (e.g., MSE, cross-entropy)---directly represents the difference.

Evaluate Options:

A: Optimizer minimizes the cost, not the cost itself---incorrect.

B: Fit executes training, not error definition---incorrect.

C: Update is vague and not a standard ML term for this---incorrect.

D: Cost function (e.g., loss) measures prediction vs. target---correct.

Reasoning: The cost function (or loss function) is the mathematical representation of error, guiding optimization.

Conclusion: D is the correct answer.

In OCI Data Science, the documentation explains: ''The cost function (or loss function) measures the difference between the model's predicted values and the actual target values, such as mean squared error for regression or cross-entropy for classification.'' Optimizers (A) use this to adjust weights, fit (B) is a training step, and update (C) isn't a defined function here---only the cost function (D) fits the description. This aligns with standard ML terminology and OCI's AutoML processes.

: Oracle Cloud Infrastructure Data Science Documentation, 'Machine Learning Concepts - Cost Functions'.

Answer: B

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'.

Answer: B

Explanation:

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'.