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Oracle 1Z0-1110-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q129-Q134):

NEW QUESTION # 129
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 as command-line arguments and create a new job every time you run the code
B. 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
C. Create a new job every time you need to run your code and pass the parameters as environment variables
D. Create a new job by setting the required parameters in your code and create a new job for every code change

Answer: B

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 # 130
Which of these options allow the sharing and loading back of ML models into a notebook session?

A. Model taxonomy
B. Model deployment
C. Model catalog
D. Model provenance

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the mechanism for sharing and reloading ML models in OCI Data Science.
Evaluate Options:
A . Model provenance: Tracks model origin—informative but not a sharing mechanism.
B . Model taxonomy: Categorizes models (e.g., regression)—not for sharing/loading.
C . Model deployment: Makes models accessible as endpoints, not for notebook reloading.
D . Model catalog: Stores models and artifacts, enabling sharing and loading into sessions.
Reasoning: The Model Catalog is OCI’s centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK).
Conclusion: D is the correct tool.
The OCI Model Catalog “enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation.” Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".

NEW QUESTION # 131
Which OCI service enables you to build, train, and deploy machine learning models in the cloud?

A. Oracle Cloud Infrastructure Data Flow
B. Oracle Cloud Infrastructure Data Science
C. Oracle Cloud Infrastructure Data Integration
D. Oracle Cloud Infrastructure Data Catalog

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for ML model lifecycle.
Evaluate Options:
A: Data Catalog—Metadata management, not ML.
B: Data Integration—ETL, not ML.
C: Data Science—Full ML lifecycle—correct.
D: Data Flow—Spark processing, not full ML.
Reasoning: C supports building, training, deploying models.
Conclusion: C is correct.
OCI documentation states: “OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog.” A, B, and D serve other purposes—only C fits the ML lifecycle per OCI’s offerings.
1: Oracle Cloud Infrastructure Data Science Documentation, "Service Overview".

NEW QUESTION # 132
Which of the following best describes the principal goal of data science?

A. To collect and archive exhaustive datasets from various source systems for corporate record-keeping uses.
B. To mine and analyze large amounts of data in order to uncover information that can be used for operational improvements and business gains.
C. To collect and prepare data for use as part of analytics applications.
D. Data science is focused on output of the analysis.

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Define data science’s main goal.
Evaluate Options:
A: Archiving—Not the focus; too narrow.
B: Analyze for insights/business value—Core purpose—correct.
C: Prep for analytics—Means, not the end goal.
D: Output-focused—Vague, incomplete.
Reasoning: B captures the actionable insight generation central to data science.
Conclusion: B is correct.
OCI documentation defines data science as “mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value.” A is storage-focused, C is preparatory, and D is unclear—only B reflects the principal goal per OCI’s mission.
1: Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".

NEW QUESTION # 133
Where are OCI secrets stored?

A. OCI Vault
B. Oracle Databases
C. OCI Object Storage
D. Autonomous Data Warehouse

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
Define OCI Secrets: Secrets are sensitive data (e.g., API keys, passwords) managed securely in OCI.
Evaluate Options:
A: Object Storage is for general data, not secure secret management.
B: Vault is OCI’s service for storing and managing secrets securely.
C: Autonomous Data Warehouse is for analytics, not secret storage.
D: Oracle Databases store data, not OCI-specific secrets.
Reasoning: Vault is purpose-built for secrets with encryption and access control.
Conclusion: B is correct.
OCI Vault “provides a secure, centralized service to store and manage secrets, such as passwords and keys, with encryption at rest and fine-grained access policies.” Object Storage (A), Autonomous Data Warehouse (C), and Oracle Databases (D) serve other purposes—only Vault (B) is designed for secrets per OCI’s security architecture.
1: Oracle Cloud Infrastructure Vault Documentation, "Secrets Management".

NEW QUESTION # 134
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