Oracle 1Z0-1110-26 PDF Demo - 1Z0-1110-26 Prüfungsinformationen

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Oracle 1Z0-1110-26 Exam Syllabus Topics:

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

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1Z0-1110-26 Prüfungsinformationen, 1Z0-1110-26 Exam Fragen

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Oracle Cloud Infrastructure Data Science Professional 1Z0-1110-26 Prüfungsfragen mit Lösungen (Q83-Q88):

83. Frage
You want to make API calls against other OCI services from your instance without configuring user credentials. How would you achieve this?

A. Create a group and add a policy
B. No configuration is required for making API calls
C. Create a dynamic group and add a policy
D. Create a dynamic group and add your instance

Antwort: C

Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Enable credential-less API calls from an instance.
Understand Resource Principal: Allows instances to authenticate via IAM without user creds.
Evaluate Options:
A: Dynamic group + policy—Correct; groups instance, grants access.
B: Dynamic group only—Incomplete; needs policy.
C: User group—Irrelevant for instances.
D: No config—False; setup required.
Reasoning: A sets up resource principal fully—group and perms.
Conclusion: A is correct.
OCI documentation states: “To make API calls without credentials, create a dynamic group including the instance and add a policy (A) granting access to OCI services—enables resource principal.” B lacks policy, C is user-based, D is false—only A completes the process per OCI’s IAM setup.
1: Oracle Cloud Infrastructure IAM Documentation, "Resource Principal Configuration".

84. Frage
Which components are a part of the OCI Identity and Access Management service?

A. Policies
B. Compute instances
C. VCN
D. Regional subnets

Antwort: A

Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify IAM components in OCI.
Understand IAM: Manages access via policies, groups, etc.
Evaluate Options:
A: Policies—Core IAM component—correct.
B: Subnets—Networking, not IAM.
C: Instances—Compute, not IAM.
D: VCN—Networking, not IAM.
Reasoning: Only A is an IAM element—others are resources.
Conclusion: A is correct.
OCI documentation states: “IAM includes components like policies (A), groups, and compartments to control resource access.” B, C, and D are infrastructure, not IAM—only A fits per OCI’s IAM framework.
1: Oracle Cloud Infrastructure IAM Documentation, "IAM Components".

85. Frage
In machine learning, what is the primary difference between supervised and unsupervised learning?

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

Antwort: B

Begründung:
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".

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

Antwort: D

Begründung:
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".

87. Frage
You are a data scientist designing an air traffic control model, and you choose to leverage Oracle AutoML. You understand that the Oracle AutoML pipeline consists of multiple stages and automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML pipeline?

A. Algorithm selection, Adaptive sampling, Feature selection, Hyperparameter tuning
B. Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning
C. Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning
D. Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning

Antwort: D

Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Sequence OCI AutoML pipeline stages.
Stages:
Adaptive sampling: Reduces data size if large.
Feature selection: Picks relevant features.
Algorithm selection: Chooses best model type.
Hyperparameter tuning: Optimizes model params.
Evaluate: C (sampling, features, algorithms, tuning) matches logical flow—data first, then model.
Reasoning: Sampling precedes feature work—standard in OCI.
Conclusion: C is correct.
OCI documentation states: “AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C).” Sampling reduces data first, then features and models are optimized—other orders (A, B, D) misalign with OCI’s sequence.
1: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".

88. Frage
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