Free Oracle 1z0-1110-25 Practice Exam with Questions & Answers | Set: 4

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".

Detailed Answer in Step-by-Step Solution:

Define Git: Git is a version control system—centralized vs. distributed is key.

Evaluate Options:

A: Incorrect—Git is distributed, not centralized (e.g., SVN is centralized).

B: Correct—Distributed, tracks file changes across local and remote repos.

C: Incorrect—Git allows simultaneous contributions; it manages, not prevents, merges.

D: Incorrect—Centralized is wrong, and “copious data” is vague.

Reasoning: Git’s distributed nature (each user has a full repo copy) and change-tracking are core traits.

Conclusion: B is accurate.

OCI documentation aligns with Git’s official definition: “Git is a distributed version control system that tracks changes to files, enabling collaboration and version history management.” A and D misclassify it as centralized, while C misrepresents merge handling—B captures Git’s essence as used in OCI Data Science.

Oracle Cloud Infrastructure Code Repository Documentation, "Git Overview".

Detailed Answer in Step-by-Step Solution:

Step 1: Install, configure, and authenticate Git: Git must be installed and configured (e.g., git config --global user.name) before any repository actions.

Step 2: Configure SSH keys: SSH keys are set up (e.g., ssh-keygen) and added to the remote service (e.g., GitHub, OCI Code Repository) for secure access.

Step 3: Create local and remote Git repository: Initialize a local repo (git init) and create/link a remote repo (e.g., git remote add origin).

Step 4: Commit files: Add files (git add .) and commit them locally (git commit -m "message").

Step 5: Push to remote: Push local commits to the remote repo (git push origin main).

Evaluate Options: Only D (1, 2, 3, 4, 5) follows this logical sequence; others (e.g., A starts with SSH before Git installation) are illogical.

The standard Git workflow in OCI Data Science or general practice begins with installing Git (1), configuring SSH for secure access (2), creating repositories (3), committing locally (4), and pushing remotely (5). The OCI Code Repository documentation aligns with this: “First, install Git and configure authentication (e.g., SSH), then set up repositories and manage code.” D is the only option reflecting this industry-standard process.

Oracle Cloud Infrastructure Code Repository Documentation, "Git Workflow Basics".

Detailed Answer in Step-by-Step Solution:

Objective: Select an ADSTuner strategy to minimize cost and assess model quality.

Understand ADSTuner: Optimizes hyperparameters with configurable search spaces.

Evaluate Options:

A: Detailed—Exhaustive, high cost—incorrect.

B: No search space—False; tuning requires a space.

C: Perfunctory—Quick, low-cost assessment—correct.

D: Dictionary—Defines space but not a strategy.

Reasoning: Perfunctory balances cost and initial quality check.

Conclusion: C is correct.

OCI documentation states: “ADSTuner’s perfunctory strategy (C) performs a quick, low-cost search to assess model quality, ideal for initial tuning.” Detailed (A) is costly, B misstates requirements, and D is a method, not a strategy—only C fits the goal.

Oracle Cloud Infrastructure ADS SDK Documentation, "ADSTuner Search Strategies".

Detailed Answer in Step-by-Step Solution:

Objective: Understand parallel execution in OCI Pipelines.

Evaluate Options:

A: False—Pipelines support parallelism.

B: True—DAG allows sequential/parallel steps—correct.

C: False—Not all steps must be parallel.

D: False—Independence enables parallelism.

Reasoning: B reflects OCI’s DAG-based flexibility.

Conclusion: B is correct.

OCI documentation states: “Pipeline steps can run sequentially or in parallel, defined by a directedacyclic graph (DAG) (B), allowing optimization of completion time.” A, C, and D misrepresent this—only B aligns with OCI’s pipeline design.

Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Parallelism".

Detailed Answer in Step-by-Step Solution:

Objective: Sequence steps for a PySpark app in OCI Data Science.

Evaluate Steps:

Launch notebook: First—provides the environment.

Install PySpark conda: Second—sets up Spark libraries.

Configure core-site.xml: Third—connects to data (e.g., Object Storage).

Develop app: Fourth—writes the PySpark code.

Data Flow: Fifth—optional scaling, post-development.

Check Options: D (1, 2, 3, 4, 5) matches this logical flow.

Reasoning: Notebook first, then setup, coding, and scaling.

Conclusion: D is correct.

OCI documentation recommends: “1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale.” D follows this—others (A, B, C) misorder critical steps like launching the notebook.

Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".

Detailed Answer in Step-by-Step Solution:

Understand Published Conda Environments: In OCI Data Science, these are custom conda environments shared across users via Object Storage.

Evaluate Options:

A: Vague—All conda environments can address use cases; not unique to “published.”

B: Incorrect—Availability on reactivation applies to session persistence, not publishing.

C: Correct—Publishing saves the environment to Object Storage for sharing/reuse.

D: Incorrect—Block volumes store session data, not published environments.

Reasoning: The unique aspect of “published” environments is their storage in Object Storage (via odsc conda publish), enabling team access.

Conclusion: C is the distinctive feature.

The OCI Data Science documentation highlights that “published conda environments are saved to an OCI Object Storage Bucket, allowing them to be shared across notebook sessions and users.” This distinguishes C from A (generic), B (session-related), and D (block volume is for session state, not publishing). Publishing to Object Storage is the defining trait per Oracle’s design.

Oracle Cloud Infrastructure Data Science Documentation, "Managing Conda Environments - Publishing" section.

Detailed Answer in Step-by-Step Solution:

Objective: Define the role of score.py in OCI model artifacts.

Understand Artifacts: score.py is key for deployment runtime.

Evaluate Options:

A: Infra config—Handled by OCI settings, not score.py.

B: Inference logic—Correct; runs load_model(), predict().

C: Scaling—Set in deployment, not score.py.

D: Dependencies—In runtime.yaml, not score.py.

Reasoning: B aligns with score.py’s execution role.

Conclusion: B is correct.

OCI documentation states: “score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint.” A, C, and D are managed elsewhere—only B matches OCI’s design.

Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".

Detailed Answer in Step-by-Step Solution:

Objective: Identify OCI’s managed Kubernetes service.

Evaluate Options:

A: Container Registry—Stores images, not Kubernetes.

B: Load Balancing—Network service, not Kubernetes.

C: Container Engine (OKE)—Managed Kubernetes—correct.

D: Streaming—Data streaming, not containers.

Reasoning: C is OCI’s Kubernetes offering—OKE.

Conclusion: C is correct.

OCI documentation states: “OCI Container Engine for Kubernetes (OKE) (C) provides a managed service to deploy, scale, and manage containerized applications using Kubernetes.” A, B, and D serve other purposes—only C matches per OCI’s container services.

Oracle Cloud Infrastructure OKE Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify a step unique to MLOps vs. DevOps.

Compare MLOps and DevOps:

DevOps: Focuses on software deployment (CI/CD).

MLOps: Extends DevOps to ML, adding model-specific steps.

Evaluate Options:

A: Continuous deployment—Common to both (software/models).

B: Continuous integration—Common to both (code merging).

C: Continuous delivery—Common to both (releasing updates).

D: Continuous training—Unique to MLOps (retraining models with new data).

Reasoning: Only D addresses ML-specific needs (model retraining).

Conclusion: D is correct.

OCI documentation notes: “MLOps extends DevOps with continuous training, a process unique to machine learning where models are retrained with new data to maintain performance.” CI (B), CD (A), and delivery (C) are shared with DevOps—only continuous training (D) is MLOps-specific.

Oracle Cloud Infrastructure Data Science Documentation, "MLOps Concepts".