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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
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%	- Supervised learning 1. Regression 2. Classification - Unsupervised learning 1. Dimensionality reduction 2. Clustering
MLOps and OCI Integration	20%	- Automation and pipelines 1. CI/CD integration 2. Model lifecycle management - OCI ecosystem 1. IAM and security 2. Object Storage

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Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q77-Q82):

NEW QUESTION # 77
In machine learning, what is the primary difference between supervised and unsupervised learning?

A. Supervised learning is created and managed by the Data Engineer.
B. Supervised learning involves data that has been labeled and classified, while unsupervised learning data is unlabeled and unclassified.
C. Supervised learning is monitored closely by data scientists, while they don’t play a role inunsupervised learning.
D. Supervised learning is only used for image recognition, while unsupervised learning can be used for various analytics applications.

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the key difference between supervised and unsupervised learning.
Define Types:
Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.
Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).
Evaluate Options:
A: Labeled vs. unlabeled—Core distinction, correct.
B: Monitoring—Misleading, not the primary difference.
C: Image recognition—False, supervised applies broadly.
D: Data Engineer—Irrelevant to learning type.
Reasoning: A captures the foundational data difference.
Conclusion: A is correct.
OCI documentation states: “Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns.” B, C, and D misrepresent this—only A aligns with OCI’s ML definitions and industry standards.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".

NEW QUESTION # 78
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 a new job every time you need to run your code and pass the parameters as environment variables
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 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

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs.
Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args).
Evaluate Options:
A: New job per run with env vars—Redundant job creation, inefficient.
B: New job per run with args—Similarly inefficient due to repeated setup.
C: Hardcode params, new job per change—Highly inefficient, requires code edits.
D: Single job, flexible params via env vars or args—Efficient, reusable—correct.
Reasoning: D minimizes job creation, allows runtime flexibility via configuration—best practice.
Conclusion: D is correct.
OCI documentation states: “For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK—most efficient approach.” Options A, B, and C involve unnecessary job proliferation or code changes—only D aligns with OCI’s design for parameterized runs.
1: Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".

NEW QUESTION # 79
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. OneHotEncoder()
B. show_in_notebook()
C. DataFrameLabelEncode()
D. auto_transform()

Answer: A

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 # 80
You are working as a data scientist for a healthcare company. They decide to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended stepsto develop a PySpark application in Oracle Cloud Infrastructure (OCI) Data Science?

A. Launch a notebook session, configure core-site.xml, install a PySpark conda environment, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
B. Launch a notebook session, install a PySpark conda environment, configure core-site.xml, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
C. Install a Spark conda environment, configure core-site.xml, launch a notebook session, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application
D. Configure core-site.xml, install a PySpark conda environment, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application, launch a notebook session

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Sequence steps for PySpark app development.
Steps:
Launch notebook: First—sets up environment.
Install PySpark conda: Second—adds Spark libraries.
Configure core-site.xml: Third—connects to data.
Develop app: Fourth—writes code.
Data Flow: Fifth—scales (optional).
Evaluate: D (1, 2, 3, 4, 5) matches this logical order.
Reasoning: Notebook first, then setup and coding.
Conclusion: D is correct.
OCI documentation states: “1) Launch a notebook session, 2) install a PySpark conda env, 3) configure core-site.xml, 4) develop your PySpark app, 5) optionally use Data Flow (D).” Other orders (A, B, C) misplace notebook launch or config—D is correct.
1: Oracle Cloud Infrastructure Data Science Documentation, "PySpark Development".

NEW QUESTION # 81
Which is NOT a compliance document?

A. Attestation
B. Penetration test report
C. Bridge letter
D. Certificate

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a non-compliance document in OCI context.
Understand Compliance Docs: Formal attestations of adherence (e.g., SOC, ISO).
Evaluate Options:
A: Certificate—Proof of compliance (e.g., ISO)—compliance doc.
B: Pen test report—Security test result, not formal compliance—correct.
C: Attestation—Statement of compliance—compliance doc.
D: Bridge letter—Links audit periods—compliance doc.
Reasoning: B is operational, not a compliance artifact.
Conclusion: B is correct.
OCI documentation lists “compliance documents like certificates (A), attestations (C), and bridge letters (D) for standards like SOC or ISO; penetration test reports (B) are security assessments, not formal compliance docs.” Only B stands apart per OCI’s compliance terminology.
1: Oracle Cloud Infrastructure Compliance Documentation, "Compliance Artifacts".

NEW QUESTION # 82
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