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| Section | Weight | Objectives |
|---|---|---|
| MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Machine Learning Fundamentals | 20% | - Supervised learning
|
| Model Development and Deployment | 30% | - Model deployment
|
| OCI Data Science Service | 30% | - Model catalog
|
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NEW QUESTION # 14
As a data scientist for a hardware company, you have been asked to predict the revenue demand for the upcoming quarter. You develop a time series forecasting model to analyze the dat a. Select the correct sequence of steps to predict the revenue demand values for the upcoming quarter.
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Prepare Model: Build and train the time series model using historical data.
Verify: Validate the model’s accuracy (e.g., using metrics like MAE or RMSE).
Save: Store the trained model (e.g., in the OCI Model Catalog).
Deploy: Make the model available for predictions (e.g., via OCI Model Deployment).
Predict: Generate revenue forecasts for the upcoming quarter.
Evaluate Options: D follows this logical flow; others (e.g., A starts with “verify” before preparation) don’t.
In OCI Data Science, the workflow for time series forecasting involves preparing the model (training), verifying its performance, saving it to the catalog, deploying it, and then predicting. This sequence is standard for ML deployment in OCI, as per the documentation. (Oracle Cloud Infrastructure Data Science Documentation, "Time Series Forecasting Workflow").
NEW QUESTION # 15
Which OCI Data Science interaction method can function without the need of scripting?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI Data Science interaction method that doesn’t require scripting.
Understand Interaction Methods: OCI provides multiple ways to interact with Data Science services—some are GUI-based, others script-based.
Evaluate Options:
A . OCI Console: A web-based graphical interface allowing users to manage resources (e.g., create notebook sessions, deploy models) via point-and-click—no scripting needed.
B . CLI: Command Line Interface requires writing commands (scripts) to execute tasks (e.g., oci data-science notebook-session create).
C . Language SDKs: Software Development Kits (e.g., Python SDK) require coding to interact programmatically (e.g., oci.data_science.DataScienceClient).
D . REST APIs: Application Programming Interfaces require scripted HTTP requests (e.g., using curl or a programming language).
Reasoning: Only the OCI Console (A) offers a no-code, user-friendly interface, while B, C, and D rely on scripting or programming.
Conclusion: A is the correct answer as it eliminates the need for scripting.
The OCI Console is described in the documentation as “a browser-based interface that allows users to manage OCI Data Science resources, such as creating notebook sessions or jobs, without writing code or scripts.” In contrast, the CLI (B) requires command-line scripts, SDKs (C) need programming (e.g., Python), and REST APIs (D) involve scripted API calls. The Console’s GUI distinguishes it as the only option functioning without scripting, aligning with Oracle’s design for accessibility to non-programmers.
1: Oracle Cloud Infrastructure Data Science Documentation, "Getting Started with OCI Console" section.
NEW QUESTION # 16
On which option do you set Oracle Cloud Infrastructure Budget?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine where OCI budgets are set.
Understand Budgets: Track spending across OCI resources.
Evaluate Options:
A: Compartments—Scoped within tenancy, not budget root.
B: Instances—Specific resources, not budget scope.
C: Tags—Filter costs, not budget setting.
D: Tenancy—Top-level scope for budgets—correct.
Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
Conclusion: D is correct.
OCI documentation states: “Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending.” A, B, and C are sub-elements—only D is the primary scope per OCI’s cost management.
1: Oracle Cloud Infrastructure Cost Management Documentation, "Setting Budgets".
NEW QUESTION # 17
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?
Answer: B,C,D
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. (Oracle Cloud Infrastructure Data Labeling Service Documentation, "Supported Data Types").
NEW QUESTION # 18
As you are working in your notebook session, you find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Scale up notebook without losing work.
Evaluate Options:
A: Object Storage—Extra steps, inefficient.
B: Block volume—Persists data, seamless scale—correct.
C: Local machine—Risky, cumbersome.
D: Recreate—Loses work, impractical.
Reasoning: B uses OCI’s built-in persistence.
Conclusion: B is correct.
OCI documentation states: “Files in /home/datascience (B) persist on block volume; deactivate, then reactivate with a larger shape to scale up without data loss.” A, C, and D add complexity or risk—only B is optimal per OCI’s design.
1: Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".
NEW QUESTION # 19
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