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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning Fundamentals	20%	- Supervised learning 1. Classification 2. Regression - Unsupervised learning 1. Dimensionality reduction 2. Clustering
Topic 2: Model Development and Deployment	30%	- Model training 1. Hyperparameter optimization 2. Experiments - Model deployment 1. Deployment creation 2. Prediction endpoints
Topic 3: OCI Data Science Service	30%	- Projects and notebooks 1. Notebook sessions 2. Conda environments - Model catalog 1. Model metadata 2. Model versioning
Topic 4: MLOps and OCI Integration	20%	- Automation and pipelines 1. Model lifecycle management 2. CI/CD integration - OCI ecosystem 1. Object Storage 2. IAM and security

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Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q24-Q29):

NEW QUESTION # 24
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?

A. Create your code to expect different parameters either as environment variables or as command-line arguments, which are set on every job run with different values
B. Create a new job by setting the required parameters in your code and create a new job for every code change
C. Create your code to expect different parameters as command-line arguments and create a new job every time you run the code
D. Create a new job every time you need to run your code and pass the parameters as environment variables

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently vary hyperparameters in OCI Jobs.
Evaluate Options:
A: New job per run—Wastes setup time.
B: Code changes per job—Inefficient, error-prone.
C: Flexible params per run—Efficient, reusable—correct.
D: New job per run—Redundant effort.
Reasoning: C minimizes job creation, maximizes flexibility.
Conclusion: C is correct.
OCI documentation states: “For varying hyperparameters, configure a single Job with code accepting environment variables or command-line arguments (C), set per run—most efficient.” A and D over-create jobs, B ties params to code—only C optimizes.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Parameterization".

NEW QUESTION # 25
As a data scientist, you are working on a global health dataset that has data from more than 50 countries. You want to encode three features, such as 'countries', 'race', and 'body organ' as categories. Which option would you use to encode the categorical feature?

A. auto_transform()
B. show_in_notebook()
C. OneHotEncoder()
D. DataFrameLabelEncode()

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Encode categorical features in a Data Science context (likely ADS SDK).
Understand Encoding: Converts categories (e.g., countries) to numerical forms.
Evaluate Options:
A: Not a standard ADS method—incorrect.
B: General transformation, not specific encoding—incorrect.
C: OneHotEncoder—Standard for categorical encoding—correct.
D: Visualization, not encoding—incorrect.
Reasoning: One-hot encoding creates binary columns—ideal for multiple categories.
Conclusion: C is correct.
OCI documentation states: “In ADS SDK, use OneHotEncoder (C) from sklearn (or similar) to encode categorical features like ‘countries’ into binary vectors for modeling.” A isn’t real, B is too broad, D is unrelated—only C fits OCI’s encoding practice.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Encoding with ADS".

NEW QUESTION # 26
You are a data scientist; you use the Oracle Cloud Infrastructure (OCI) Language service to train custom models. Which types of custom models can be trained?

A. Object detection, Text classification
B. Sentiment Analysis, Named Entity Recognition (NER)
C. Text classification, Named Entity Recognition (NER)
D. Image classification, Named Entity Recognition (NER)

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify custom model types for OCI Language.
Understand OCI Language: Focuses on text analysis.
Evaluate Options:
A: Image classification—Not text-based, incorrect.
B: Text classification, NER—Both text tasks—correct.
C: Sentiment, NER—Sentiment is pretrained, not custom.
D: Object detection—Image-based, incorrect.
Reasoning: B aligns with OCI Language’s text custom models.
Conclusion: B is correct.
OCI Language documentation states: “Custom models can be trained for text classification and Named Entity Recognition (NER) using your data.” Image tasks (A, D) are for Vision, and sentiment (C) is pretrained—only B fits OCI Language’s scope.
1: Oracle Cloud Infrastructure Language Documentation, "Custom Model Training".

NEW QUESTION # 27
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. 1, 2, 3, 4, 5
B. 3, 5, 1, 2, 4
C. 4, 2, 3, 1, 5
D. 2, 3, 1, 4, 5

Answer: A

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

NEW QUESTION # 28
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 a single label to an image
B. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
C. Adding labels to an image using object detection, by drawing bounding boxes to an image
D. Adding multiple labels to an image
E. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image

Answer: A,C,D

Explanation:
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.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".

NEW QUESTION # 29
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