Curious about Actual Oracle Cloud (1Z0-1110-25) Exam Questions?

Correct : A, D, E

Detailed Answer in Step-by-Step Solution:

Objective: Prevent deployment failure due to utilization limits.

Understand Utilization: High load requires capacity or throttling.

Evaluate Options:

A: More instances---Scales horizontally---correct.

B: Delete---Stops service, not a fix---incorrect.

C: Fewer instances---Worsens issue---incorrect.

D: Larger VM---Scales vertically---correct.

E: Reduce bandwidth---Controls load---correct.

Reasoning: A and D increase capacity, E manages demand---effective trio.

Conclusion: A, D, E are correct.

OCI documentation states: ''To avoid utilization limits, increase instances (A), use a larger compute shape (D), or reduce load balancer bandwidth (E) to manage request rates.'' B stops service, C reduces capacity---only A, D, E align with OCI's deployment scaling options.

: Oracle Cloud Infrastructure Data Science Documentation, 'Model Deployment Scaling'.

Correct : C

Detailed Answer in Step-by-Step Solution:

Objective: Identify the interactive coding environment in OCI Data Science.

Evaluate Options:

A: Model Catalog stores models---not for coding.

B: Jobs run predefined tasks---not interactive.

C: Notebook Sessions provide JupyterLab for coding and training---interactive.

D: Projects organize work---not a coding environment.

Reasoning: Notebook Sessions are OCI's Jupyter-based tool for interactive ML development.

Conclusion: C is correct.

OCI Data Science Notebook Sessions ''provide an interactive JupyterLab environment where data scientists can write code, explore data, and train machine learning models.'' Model Catalog (A) is for storage, Jobs (B) for automation, and Projects (D) for organization---only C offers interactivity.

: Oracle Cloud Infrastructure Data Science Documentation, 'Notebook Sessions Overview'.

Correct : B, D, E

Detailed Answer in Step-by-Step Solution:

Objective: Identify three annotation methods in OCI Data Labeling for images.

Understand Data Labeling: Supports image annotations for ML.

Evaluate Options:

A: Semantic segmentation with boxes---Incorrect; segmentation is pixel-based, not boxes.

B: Single label (classification)---Supported---correct.

C: No bounding boxes---False; boxes are supported.

D: Object detection with boxes---Supported---correct.

E: Multiple labels (multi-label)---Supported---correct.

Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.

Conclusion: B, D, E are correct.

OCI documentation states: ''Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E).'' A misdefines segmentation, C contradicts support---only B, D, E are valid per OCI's Data Labeling features.

: Oracle Cloud Infrastructure Data Labeling Documentation, 'Image Annotation Types'.

Correct : D

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'.

Correct : C, D

Detailed Answer in Step-by-Step Solution:

Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.

Evaluate Metrics:

A . Mean squared error: Used for regression, not classification.

B . Explained variance score: Regression metric, not suitable.

C . Recall: Measures true positive rate per class---key for classification.

D . F1-score: Balances precision and recall---widely used in multiclass.

E . R-squared: Regression metric, not applicable.

Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.

Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Reference: Oracle Cloud Infrastructure Data Science Documentation, 'AutoML Metrics').