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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Weight	Objectives
OCI Data Science Service	30%	- Model catalog 1. Model versioning 2. Model metadata - Projects and notebooks 1. Conda environments 2. Notebook sessions
Machine Learning Fundamentals	20%	- Unsupervised learning 1. Clustering 2. Dimensionality reduction - Supervised learning 1. Classification 2. Regression
Model Development and Deployment	30%	- Model deployment 1. Deployment creation 2. Prediction endpoints - Model training 1. Experiments 2. Hyperparameter optimization
MLOps and OCI Integration	20%	- Automation and pipelines 1. Model lifecycle management 2. CI/CD integration - OCI ecosystem 1. IAM and security 2. Object Storage

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Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q137-Q142):

NEW QUESTION # 137
What happens when a notebook session is deactivated?

A. The block volume attached to the notebook is permanently deleted
B. The underlying compute instance stops
C. The data on boot volume is preserved
D. Compute cost increases due to frequent deactivation

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Notebook Sessions: These are OCI compute instances running JupyterLab.
Deactivation Impact: Deactivating stops the session to save costs.
Evaluate Options:
A: False—Costs decrease as compute stops.
B: False—Boot volume data isn’t preserved; block volume data is.
C: True—The compute instance shuts down, halting billing.
D: False—Block volume persists unless explicitly deleted.
Reasoning: Deactivation stops the instance (C), preserving block volume data separately.
Conclusion: C is correct.
The OCI documentation states: “When a notebook session is deactivated, the underlying compute instance stops, and billing for compute resources ceases. Data on the attached block volume is preserved, but the boot volume is not.” A is backwards, B misattributes preservation, and D overstates deletion—only C aligns with the process.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Lifecycle".

NEW QUESTION # 138
During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most efficient way to do this with Data Science Jobs?

A. Your code using too much disk space. Refactor the code to identify the problem
B. Create a new job with increased storage size and then run the job
C. Edit the job, change the size of the storage of your job, and start a new job run
D. On the job run, set the environment variable that helps increase the size of the storage

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently increase storage for an OCI Job.
Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable.
Evaluate Options:
A: False—Jobs can’t edit storage post-creation; it’s fixed.
B: False—No environment variable adjusts storage size.
C: True—Create a new job with larger storage (e.g., 200 GB) and run it.
D: False—Refactoring code is inefficient compared to increasing storage.
Reasoning: C is the standard OCI process for adjusting resources.
Conclusion: C is correct.
OCI documentation states: “Storage size for a Data Science Job is specified during job creation (e.g., block volume size). To increase it, create a new job with a larger storage configuration and initiate a new run.” Editing (A) isn’t supported, variables (B) don’t apply, and refactoring (D) avoids the issue—only C is efficient.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".

NEW QUESTION # 139
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image dat a. Which of the following THREE are possible ways to annotate an image in Data Labeling?

A. Adding labels to an image using object detection, by drawing bounding boxes to an image
B. Adding multiple labels to an image
C. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image
D. Adding a single label to an image
E. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling

Answer: A,B,D

NEW QUESTION # 140
You have received machine learning model training code, without clear information about the optimal shape to run the training. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?

A. Start with a random compute shape and monitor the utilization metrics and time required to finish the model training. Perform model training optimizations and performance tests in advance to identify the right compute shape before running the model training as a job
B. Start with a smaller shape and monitor the Job Run metrics and time required to complete the model training. If the compute shape is not fully utilized, tune the model parameters, and rerun the job. Repeat the process until the shape resources are fully utilized
C. Start with a smaller shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and rerun the job. Repeat the process until the processing time does not improve
D. Start with the strongest compute shape Jobs support and monitor the Job Run metrics and time required to complete the model training. Tune the model so that it utilizes as much compute resources as possible, even at an increased cost

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Optimize compute shape for cost and time.
Evaluate Options:
A: Tuning params—Focuses on model, not shape.
B: Strongest shape—Costly, unbalanced.
C: Scale up when utilized—Balances cost/time—correct.
D: Random start—Unsystematic.
Reasoning: C iteratively optimizes based on utilization.
Conclusion: C is correct.
OCI documentation advises: “Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes—optimizes cost and speed.” A misfocuses, B overspends, D lacks method—only C aligns.
1: Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".

NEW QUESTION # 141
Which Oracle Cloud Infrastructure (OCI) Data Science policy is invalid?

A. Allow dynamic-group DataScienceDynamicGroup to manage data-science-family in compartment DataScience
B. Allow group DataScienceGroup to use virtual-network-family in compartment DataScience
C. Allow group DataScienceGroup to use data-science-model-sessions in compartment DataScience
D. Allow dynamic-group DataScienceDynamicGroup to manage data-science-projects in compartment DataScience

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Spot the invalid OCI IAM policy.
Understand Policies: Use verbs (e.g., use, manage) and resource types.
Evaluate Options:
A: Valid—virtual-network-family is a resource type.
B: Invalid—No data-science-model-sessions resource type; likely meant data-science-models.
C: Valid—data-science-projects is correct.
D: Valid—data-science-family aggregates Data Science resources.
Reasoning: B uses a nonexistent resource type—error in syntax.
Conclusion: B is incorrect.
OCI IAM documentation lists valid resource types: “data-science-models, data-science-projects, data-science-family—but not data-science-model-sessions (B).” A, C, D are correct—B is invalid due to this typo/misunderstanding.
1: Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".

NEW QUESTION # 142
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