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Oracle 1z0-1096-22 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Oracle Machine Learning Notebooks | - SQL and Python integration in OML - Using OML Notebooks for data science workflows |
| Topic 2: Model Building and Evaluation | - Model evaluation and performance metrics - Applying machine learning algorithms in Autonomous Database |
| Topic 3: Introduction to Oracle Machine Learning and Autonomous Database | - Machine learning capabilities in Autonomous Database - Oracle Autonomous Database architecture overview |
| Topic 4: Data Preparation and Exploration | - Data loading and transformation using SQL and Oracle tools - Feature selection and data preprocessing techniques |
| Topic 5: Model Deployment and Management | - Deploying machine learning models in Oracle Autonomous Database - Model lifecycle management and monitoring |
Oracle Machine Learning using Autonomous Database 2022 Specialist Sample Questions:
Question 1
You are using Oracle Machine Learning with Autonomous Database for the first time. After creating an Autonomous Database, what is the next step that you have to perform?
A. Connect to the Database
B. Create a compartment
C. Create an OML user
D. Create an ML notebook
Question 2
Which feature of Oracle Machine Learning Notebooks should you use for data analysis, data discovery, data visualization, and collaboration on Oracle Autonomous Database?
A. Notebook
B. Job
C. Project
D. Workspace
Question 3
Which output formats are supported by the SET SQLFORMAT command? (Choose three.)
A. HTML
B. TXT
C. CSV
D. JSON
Question 4
You want to analyze the spike in sales during weekends in India (Saturday and Sunday) and also perform a similar analysis for weekends in Dubai (Friday and Saturday). You will be reusing these notebooks in similar situations over time. You do not want to share them with other users.
Which template should you use?
A. Public
B. Shared
C. Personal
D. Example
Question 5
Which three statements are true about unsupervised machine learning?
A. It can be used as a preliminary step for supervised algorithms.
B. There is no previously known result to guide the algorithm in building the model.
C. It analyzes cases where the target value is already known.
D. It uses unlabeled data.
Solutions:
| Question 1 Answer: C | Question 2 Answer: A | Question 3 Answer: A,C,D | Question 4 Answer: C | Question 5 Answer: B,C,D |
