Oracle Updated 1z0-1110-25 Exam Questions and Answers by willow-rose

Detailed Answer in Step-by-Step Solution:

Objective: Identify three required params for an OCI Data Flow app config.

Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.

Evaluate Options:

A: Archive path—Optional if script is in Object Storage—incorrect.

B: Local script path—Not needed; script is uploaded—incorrect.

C: Compartment—Required for resource scope—correct.

D: Bucket—Required for script storage/access—correct.

E: Display name—Required for app identification—correct.

Reasoning: C, D, E are mandatory metadata for Data Flow creation—script location is specified via bucket.

Conclusion: C, D, E are correct.

OCI documentation states: “To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object.” Local paths (B) or archives (A) are optional or handled separately—only C, D, E are required per OCI’s Data Flow API spec.

Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the Oracle service to sort document images by language.

Task Breakdown: Requires extracting text from images (OCR) and detecting language—two potential services involved.

Evaluate Options:

A. Oracle Digital Assistant: Builds chatbots—irrelevant to image or language processing.

B. OCI Language: Detects and classifies languages in text—ideal for sorting after text extraction.

C. OCI Speech: Transcribes audio to text—not applicable to images.

D. OCI Vision: Performs OCR to extract text from images—necessary but not sufficient for language sorting.

Reasoning: The task emphasizes “sorting by language.” OCI Vision extracts text, but OCI Language identifies the language (e.g., English, Spanish). Since the question asks for one service and focuses on sorting, OCI Language (B) is the best fit, assuming text extraction is a precursor step.

Conclusion: B is correct.

OCI Language “provides language detection and classification capabilities, enabling identification of languages in text extracted from documents,” per the documentation. OCI Vision handles OCR, but the sorting task aligns with OCI Language (B). Digital Assistant (A) and Speech (C) don’t apply, and while Vision (D) is a prerequisite, B is the primary service for language sorting as per OCI’s AI service design.

Oracle Cloud Infrastructure Language Documentation, "Language Detection Features".

Detailed Answer in Step-by-Step Solution:

Objective: Locate the lifecycle stage for transforming timestamps into categories.

Understand Lifecycle:

Model Type Selection: Choosing algorithms—post-data prep.

Model Validation: Evaluating performance—post-training.

Data Access: Retrieving raw data—pre-transformation.

Feature Engineering: Creating new features—correct.

Evaluate Options:

A: Too late—incorrect.

B: Post-model—incorrect.

C: Pre-transformation—incorrect.

D: Feature creation—correct.

Reasoning: Converting timestamps to time periods is feature engineering.

Conclusion: D is correct.

OCI documentation states: “Feature engineering (D) transforms raw data, such as timestamps into categorical variables (e.g., morning/afternoon), to enhance model inputs.” A, B, and C occur at different stages—only D fits OCI’s lifecycle for this task.

Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Stage".

Detailed Answer in Step-by-Step Solution:

Objective: Identify two OCI-developed, non-open-source JupyterLab extensions.

Understand Extensions: OCI enhances JupyterLab with proprietary tools.

Evaluate Options:

A: Environment Explorer—OCI-specific, non-open—correct.

B: Table of Contents—Open-source Jupyter—incorrect.

C: Command Palette—Open-source Jupyter—incorrect.

D: Notebook Examples—OCI-specific, non-open—correct.

E: Terminal—Open-source Jupyter—incorrect.

Reasoning: A and D are OCI proprietary; others are standard JupyterLab.

Conclusion: A and D are correct.

OCI documentation states: “OCI Data Science adds non-open-source extensions like Environment Explorer (A) for conda management and Notebook Examples (D) for sample code—both proprietary enhancements.” B, C, and E are open-source JupyterLab defaults—only A and D are OCI-specific per the notebook session design.

Oracle Cloud Infrastructure Data Science Documentation, "JupyterLab Extensions".