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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Objectives
Apply MLOps Practices	- Monitor and maintain machine learning models - Implement model lifecycle management - Use best practices for operationalizing ML solutions
Use Related OCI Services	- Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases
Design and Set Up Data Science Workspace	- Manage notebook sessions and compute resources - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects
OCI Data Science - Introduction and Configuration	- Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions
Implement End-to-End Machine Learning Lifecycle	- Prepare and manage datasets - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog

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Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q52-Q57):

NEW QUESTION # 52
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.

A. Predict, deploy, save, verify, prepare model
B. Prepare model, deploy, verify, save, predict
C. Prepare model, verify, save, deploy, predict
D. Verify, prepare model, deploy, save, predict

Answer: C

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 # 53
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?

A. Ensure your files and environments are written to the block volume storage under the /home/datascience directory, deactivate the notebook session, and activate the notebook session with a larger compute shape selected
B. Deactivate your notebook session, provision a new notebook session on a larger compute shapeand re-create all of your file changes
C. Create a temporary bucket on Object Storage, write all your files and data to Object Storage, delete your notebook session, provision a new notebook session on a larger compute shape, and copy your files and data from your temporary bucket onto your new notebook session
D. Download all your files and data to your local machine, delete your notebook session, provision a new notebook session on a larger compute shape, and upload your files from your local machine to the new notebook session

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 # 54
You are building a model and need input that represents data as morning, afternoon, or evening. However, the data contains a timestamp. What part of the Data Science lifecycle would you be in when creating the new variable?

A. Model type selection
B. Data access
C. Feature engineering
D. Model validation

Answer: C

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

NEW QUESTION # 55
Which step is unique to MLOps, as opposed to DevOps?

A. Continuous integration
B. Continuous delivery
C. Continuous training
D. Continuous deployment

Answer: C

Explanation:
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "MLOps Concepts".

NEW QUESTION # 56
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.

A. Key rotation allows you to encrypt no more than five keys at a time
B. Periodically rotating keys limits the amount of data encrypted by one key version
C. Periodically rotating keys makes it easier to reuse keys
D. Key rotation reduces risk if a key is ever compromised
E. Key rotation improves encryption efficiency

Answer: B,D

NEW QUESTION # 57
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