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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q40-Q45):

NEW QUESTION # 40
Arrange the following in the correct Git Repository workflow order:
* Install, configure, and authenticate Git.
* Configure SSH keys for the Git repository.
* Create a local and remote Git repository.
* Commit files to the local Git repository.
* Push the commit to the remote Git repository.

A. 4, 2, 3, 1, 5
B. 1, 2, 3, 4, 5
C. 2, 3, 1, 4, 5
D. 3, 5, 1, 2, 4

Answer: B

Explanation:
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".

NEW QUESTION # 41
You have been given a collection of digital files required for a business audit. They consist of several different formats that you would like to annotate using Oracle Cloud Infrastructure (OCI) Data Labeling.
Which THREE types of files could this tool annotate?

A. A collection of purchase orders for office supplies
B. Images of computer server racks
C. Video footage of a conversation in a conference room
D. An audio recording of a phone conversation
E. A typewritten document that details an annual budget

Answer: B,C,E

Explanation:
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").

NEW QUESTION # 42
Which of these protects customer data at rest and in transit in a way that allows customers to meet their security and compliance requirements for cryptographic algorithms and key management?

A. Identity Federation
B. Security controls
C. Customer isolation
D. Data encryption

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify protection for data at rest/transit with customer control.
* Evaluate Options:
* A: Controls-Broad, not specific to encryption.
* B: Isolation-Separates tenants, not crypto-focused.
* C: Encryption-Secures data, allows key management-correct.
* D: Federation-Auth sharing, not data protection.
* Reasoning: C provides crypto control (e.g., Vault keys).
* Conclusion: C is correct.
OCI documentation states: "Data encryption (C) protects data at rest and in transit, with customer-managed keys in OCI Vault meeting compliance needs." A and B are broader, D is unrelated-only C fits per OCI's security model.
Oracle Cloud Infrastructure Security Documentation, "Data Encryption".

NEW QUESTION # 43
You want to write a Python script to create a collection of different projects for your data science team. Which Oracle Cloud Infrastructure (OCI) Data Science interface would you use?

A. Mobile App
B. OCI Console
C. Command Line Interface (CLI)
D. The OCI Software Development Kit (SDK)

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Choose an interface for a Python script to manage projects.
* Evaluate Options:
* A: OCI SDK-Python-based, scriptable-correct.
* B: Console-GUI, not scriptable.
* C: CLI-Command-based, not Python-native.
* D: Mobile App-Not for scripting.
* Reasoning: A enables programmatic project creation.
* Conclusion: A is correct.
OCI documentation states: "Use the OCI Python SDK (A) to programmatically manage Data Science resources, like creating projects, via Python scripts." B, C, and D don't support Python scripting-only A fits.
Oracle Cloud Infrastructure SDK Documentation, "Data Science API".

NEW QUESTION # 44
Which model has an open-source, open model format that allows you to run machine learning models on different platforms?

A. PyTorch
B. PySpark
C. TensorFlow
D. ONNX

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify an open model format for cross-platform ML model execution.
* Evaluate Options:
* A. PySpark: A big data framework, not a model format.
* B. PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.
* C. TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.
* D. ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.
* Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.
* Conclusion: D is the correct choice.
ONNX (D) is "an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow," per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats-only ONNX ensures cross-platform compatibility.
Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".

NEW QUESTION # 45
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