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Oracle 1Z0-1110-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Deploy models and manage endpoints - Model development, training, and evaluation |
| Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science |
Oracle Cloud Infrastructure Data Science Professional Sample Questions:
What is the name of the machine learning library used in Apache Spark?
- A. HadoopML
- B. Structured Streaming
- C. GraphX
- D. MLib
Correct Answer: D 🗳️
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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. Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning
- B. Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning
- C. Algorithm selection, Adaptive sampling, Feature selection, Hyperparameter tuning
- D. Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning
Correct Answer: A 🗳️
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You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?
- A. Create the job in Data Science Jobs and start a job run. When it is done, start a new job run until you achieve the number of runs required
- B. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel workloads
- C. Create the job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
- D. Create a new job for every job run that you have to run in parallel, because the Data Science Jobs service can have only one job run per job
Correct Answer: C 🗳️
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Which statement best describes Oracle Cloud Infrastructure Data Science Jobs?
- A. Jobs let you define and run repeatable tasks on customer-managed infrastructure.
- B. Jobs let you define and run all Oracle Cloud DevOps workloads.
- C. Jobs let you define and run repeatable tasks on fully managed third-party cloud infrastructures.
- D. Jobs let you define and run repeatable tasks on fully managed infrastructure.
Correct Answer: D 🗳️
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During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most efficient way to do this with Data Science Jobs?
- A. Your code using too much disk space. Refactor the code to identify the problem
- B. Edit the job, change the size of the storage of your job, and start a new job run
- C. Create a new job with increased storage size and then run the job
- D. On the job run, set the environment variable that helps increase the size of the storage
Correct Answer: C 🗳️
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