1Z0-1110-25 EXAMS QUESTIONS AND ANSWERS & DUMPS PDF FOR ORACLE CLOUD INFRASTRUCTURE 2025 DATA SCIENCE PROFESSIONAL

1z0-1110-25 exams questions and answers & dumps PDF for Oracle Cloud Infrastructure 2025 Data Science Professional

1z0-1110-25 exams questions and answers & dumps PDF for Oracle Cloud Infrastructure 2025 Data Science Professional

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q127-Q132):

NEW QUESTION # 127
You are working as a Data Scientist for a healthcare company. You have a series of neurophysiological data on OCI Data Science and have developed a convolutional neural network (CNN) classification model. It predicts the source of seizures in drug-resistant epileptic patients. You created a model artifact with all the necessary files. When you deployed the model, it failed to run because you did not point to the correct conda environment in the model artifact. Where would you provide instructions to use the correct conda environment?

  • A. score.py
  • B. model_artifact_validate.py
  • C. runtime.yaml
  • D. requirements.txt

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine where to specify the conda environment for an OCI model deployment.
* Understand Model Deployment: Requires artifacts like score.py and runtime.yaml to define runtime settings.
* Evaluate Options:
* A. score.py: Contains inference logic (e.g., load_model(), predict())-not for environment specs.
* B. runtime.yaml: Defines deployment runtime, including conda environment path-correct.
* C. requirements.txt: Lists pip dependencies-not used in OCI for conda environments.
* D. model_artifact_validate.py: Not a standard artifact; doesn't exist in OCI deployment.
* Reasoning: runtime.yaml specifies the conda env (e.g., slug: pyspark30_p37_cpu_v2)-failure to set this causes deployment errors.
* Conclusion: B is correct.
OCI documentation states: "The runtime.yaml file in a model artifact specifies the runtime environment, including the conda environment path (e.g., ENVIRONMENT_SLUG: pyspark30_p37_cpu_v2), ensuring the deployed model uses the correct dependencies." score.py (A) handles inference, requirements.txt (C) is for pip (not conda in OCI), and D isn't valid-only B addresses the conda issue per OCI's deployment process.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".


NEW QUESTION # 128
Which two statements are true about published conda environments?

  • A. The odsc conda init command is used to configure the location of published conda environments
  • B. They are curated by Oracle Cloud Infrastructure (OCI) Data Science
  • C. In addition to service job run environment variables, conda environment variables can be used inData Science Jobs
  • D. You can only create a published conda environment by modifying a Data Science conda environment
  • E. Your notebook session acts as the source to share published conda environments with team members

Answer: A,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify true statements about published conda environments in OCI.
* Understand Published Conda: Custom envs shared via Object Storage.
* Evaluate Options:
* A: False-Users create them, not OCI-curated.
* B: True-odsc conda init sets up access to published envs.
* C: False-Object Storage, not notebook, is the source.
* D: False-Can create from scratch, not just modifications.
* E: True-Conda env vars extend job configs.
* Reasoning: B configures access, E enhances flexibility-match OCI behavior.
* Conclusion: B and E are correct.
OCI documentation states: "odsc conda init (B) configures the bucket for published conda environments, and Jobs can use conda environment variables (E) alongside service vars." A misattributes curation, C misplaces source, and D limits creation-only B and E are true per OCI's conda management.
Oracle Cloud Infrastructure Data Science Documentation, "Published Conda Environments".


NEW QUESTION # 129
How can you collaborate with team members in OCI Data Science Workspace?

  • A. By granting access to specific notebooks and files
  • B. By enabling chat and video conferencing within the workspace
  • C. By using version control systems integrated with the workspace
  • D. By sharing the workspace instance with other users

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).
* Evaluate Options:
* A: Access control-Possible but not primary collaboration.
* B: Version control (e.g., Git)-Standard for code sharing-correct.
* C: Shared instance-Not supported; sessions are single-user.
* D: Chat/video-Not a feature of OCI Data Science.
* Reasoning: B leverages Git for team collaboration-OCI's recommended method.
* Conclusion: B is correct.
OCI documentation states: "Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks." A is limited, C isn't feasible, and D isn't available- only B matches OCI's collaboration approach.
Oracle Cloud Infrastructure Data Science Documentation, "Collaboration with Git".


NEW QUESTION # 130
In which two ways can you improve data durability in Oracle Cloud Infrastructure Object Storage?

  • A. Enable server-side encryption
  • B. Setup volumes in a RAID1 configuration
  • C. Enable client-side encryption
  • D. Enable Versioning
  • E. Limit delete permissions

Answer: D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two methods to enhance Object Storage durability.
* Understand Durability: Ensures data isn't lost-focus on redundancy and protection.
* Evaluate Options:
* A: RAID1-Block volume feature, not Object Storage.
* B: Encryption-Secures data, not durability.
* C: Versioning-Retains old versions, prevents loss-correct.
* D: Limit delete-Prevents accidental deletion-correct.
* E: Client encryption-Secures, not durability-focused.
* Reasoning: C and D directly protect against data loss-durability-focused.
* Conclusion: C and D are correct.
OCI documentation states: "Improve Object Storage durability with Versioning (C) to retain previous object versions and by limiting delete permissions (D) to prevent accidental loss." A isn't applicable, B and E focus on security-only C and D enhance durability per OCI's storage features.
Oracle Cloud Infrastructure Object Storage Documentation, "Data Durability Options".


NEW QUESTION # 131
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?

  • A. Create your code to expect different parameters as command-line arguments and create a new job every time you run the code
  • B. Create a new job by setting the required parameters in your code and create a new job for every code change
  • C. Create a new job every time you need to run your code and pass the parameters as environment variables
  • D. Create your code to expect different parameters either as environment variables or as command-line arguments, which are set on every job run with different values

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Efficiently vary hyperparameters in OCI Jobs.
* Evaluate Options:
* A: New job per run-Wastes setup time.
* B: Code changes per job-Inefficient, error-prone.
* C: Flexible params per run-Efficient, reusable-correct.
* D: New job per run-Redundant effort.
* Reasoning: C minimizes job creation, maximizes flexibility.
* Conclusion: C is correct.
OCI documentation states: "For varying hyperparameters, configure a single Job with code accepting environment variables or command-line arguments (C), set per run-most efficient." A and D over-create jobs, B ties params to code-only C optimizes.
Oracle Cloud Infrastructure Data Science Documentation, "Job Parameterization".


NEW QUESTION # 132
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