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Oracle 1Z0-1110-26 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Prepare and manage datasets - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog |
| Topic 2: Apply MLOps Practices | - Monitor and maintain machine learning models - Implement model lifecycle management - Use best practices for operationalizing ML solutions |
| Topic 3: Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools |
| Topic 4: OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions |
| Topic 5: Use Related OCI Services | - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases |
Oracle Cloud Infrastructure Data Science Professional Sample Questions:
Question 1
Which statement is true about origin management in Web Application Firewall (WAF)?
A. Only statement A is true
B. Only a single origin can be active for a WAF
C. Both the statements are true
D. Both the statements are false
E. Multiple origins can be defined
F. Only statement B is true
Question 2
When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) DataScience model catalog, you create a score.py file. What is the purpose of the score.py file?
A. Define the compute scaling strategy
B. Define the inference server dependencies
C. Execute the inference logic code
D. Configure the deployment infrastructure
Question 3
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. Text classification, Named Entity Recognition (NER)
B. Object detection, Text classification
C. Sentiment Analysis, Named Entity Recognition (NER)
D. Image classification, Named Entity Recognition (NER)
Question 4
You are working as a data scientist for a healthcare company. They decided 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 steps to develop a PySpark application in 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
Question 5
What happens when a notebook session is deactivated?
A. The underlying compute instance stops
B. The data on boot volume is preserved
C. Compute cost increases due to frequent deactivation
D. The block volume attached to the notebook is permanently deleted
Solutions:
| Question 1 Answer: C | Question 2 Answer: C | Question 3 Answer: A | Question 4 Answer: B | Question 5 Answer: A |
