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Oracle 1Z0-1110-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science |
| Topic 2: Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Topic 3: Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions |
| Topic 4: Implement End-to-End Machine Learning Lifecycle | 45% | - Model development, training, and evaluation - Use AutoML and built-in algorithms - Deploy models and manage endpoints - Model saving, cataloging, and versioning - Data preparation, exploration, and transformation |
| Topic 5: Apply MLOps Practices | 20% | - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility |
Oracle Cloud Infrastructure Data Science Professional Sample Questions:
1. You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor dat a. What are you trying to determine? How does the required False Alarm Probability setting affect an anomaly detection model?
A) It determines how many false alarms occur before an error message is generated
B) It is used to disable the reporting of false alarms
C) It changes the sensitivity of the model to detecting anomalies
D) It adds a score to each signal indicating the probability that it’s a false alarm
2. You are a researcher who requires access to large datasets. Which OCI service would you use?
A) OCI Data Science
B) Oracle Open Data
C) ADW (Autonomous Data Warehouse)
D) Oracle Databases
3. You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many sub-folders and classes. What is the best way to run this project as a Job?
A) ZIP the entire code project folder, upload it as a Job artifact on job creation, and set JOB_RUN_ENTRYPOINT to point to the main executable file
B) ZIP the entire code project folder and upload it as a Job artifact. Jobs automatically identifies the main top-level where the code is run
C) Rewrite your code so that it is a single executable Python or Bash/Shell script file
D) ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file automatically
4. Which of the following best describes the principal goal of data science?
A) Data science is focused on output of the analysis.
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 archive exhaustive datasets from various source systems for corporate record-keeping uses.
D) To collect and prepare data for use as part of analytics applications.
5. While working with Git on Oracle Cloud Infrastructure (OCI) Data Science, you notice that two of the operations are taking more time than the others due to your slow internet speed. Which TWO operations would experience the delay?
A) Updating the local repo to match the content from a remote repository
B) Pushing changes to a remote repository
C) Moving the changes into staging area for the next commit
D) Making a commit that is taking a snapshot of the local repository for the next push
E) Converting an existing local project folder to a Git repository
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: A,B |
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