FREE PDF 2025 FANTASTIC ORACLE 1Z0-1110-25: SIMULATED ORACLE CLOUD INFRASTRUCTURE 2025 DATA SCIENCE PROFESSIONAL TEST

Free PDF 2025 Fantastic Oracle 1z0-1110-25: Simulated Oracle Cloud Infrastructure 2025 Data Science Professional Test

Free PDF 2025 Fantastic Oracle 1z0-1110-25: Simulated Oracle Cloud Infrastructure 2025 Data Science Professional Test

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q148-Q153):

NEW QUESTION # 148
As a data scientist, you require a pipeline to train ML models. When can a pipeline run be initiated?

  • A. Pipeline can be initiated once it is created.
  • B. Pipeline can be initiated before the active state.
  • C. Pipeline can be initiated after the active state.
  • D. Pipeline can be initiated during the pipeline run state.

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine when an OCI Data Science pipeline can start.
* Understand Pipelines: They're workflows with defined steps, executed on demand or scheduled.
* Evaluate Options:
* A: Once created, a pipeline can be run immediately-correct.
* B: "During run state" implies it's already running-illogical.
* C: "After active state" is unclear; pipelines run when triggered, not post-state.
* D: "Before active state" is vague-creation precedes running.
* Reasoning: Pipelines are executable post-creation via UI/CLI-simplest interpretation is A.
* Conclusion: A is correct.
OCI Data Science documentation states: "After a pipeline is created, you can initiate a pipeline run immediately or schedule it using the OCI Console, CLI, or SDK." B, C, and D misalign with this-running starts post-creation (A), not during/after ambiguous states.
Oracle Cloud Infrastructure Data Science Documentation, "Pipelines - Running a Pipeline".


NEW QUESTION # 149
You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage Bucket, train a model, and save it to the model catalog. How would you design the authentication mechanism for the job?

  • A. Use the resource principal of the job run as the signer in the job code, ensuring there is a dynamic group for this job run with appropriate access to Object Storage and the model catalog
  • B. Create a pre-authenticated request (PAR) for the Object Storage bucket and use that in the job code
  • C. Store your personal OCI config file and keys in the Vault, and access the Vault through the job run resource principal
  • D. Package your personal OCI config file and keys in the job artifact

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Securely authenticate a recurring OCI Job.
* Evaluate Options:
* A: PAR-Limited duration, insecure for recurring jobs.
* B: Resource principal-Secure, managed auth for Jobs-correct.
* C: Personal config-Unscalable, security risk.
* D: Vault with personal keys-Complex, still uses user creds.
* Reasoning: B uses OCI's native, secure resource principal mechanism.
* Conclusion: B is correct.
OCI documentation states: "For Jobs accessing sensitive data, use resource principals with a dynamic group (e.
g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog- secure and scalable." A is temporary, C and D risk credential exposure-B is best practice.
Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".


NEW QUESTION # 150
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor data. How does the required False Alarm Probability setting affect an anomaly detection model?

  • A. It changes the sensitivity of the model to detecting anomalies
  • B. It determines how many false alarms occur before an error message is generated
  • C. It adds a score to each signal indicating the probability that it's a false alarm
  • D. It is used to disable the reporting of false alarms

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Understand the effect of False Alarm Probability (FAP) in OCI Anomaly Detection.
* Understand FAP: Controls false positive rate-threshold for anomaly flagging.
* Evaluate Options:
* A: Disable reporting-Incorrect; FAP sets sensitivity, not on/off.
* B: Changes sensitivity-Correct; lower FAP = fewer false positives-correct.
* C: Count-based error-Incorrect; not a counter.
* D: Score per signal-Incorrect; FAP is a global setting.
* Reasoning: FAP adjusts detection threshold-direct impact on sensitivity.
* Conclusion: B is correct.
OCI documentation states: "False Alarm Probability (FAP) (B) adjusts the model's sensitivity in Anomaly Detection-lower values increase specificity, reducing false positives." A, C, and D misinterpret FAP's role- only B aligns with OCI's anomaly detection tuning.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Settings".


NEW QUESTION # 151
Which function's objective is to represent the difference between the predictive value and the target value?

  • A. Optimizer function
  • B. Cost function
  • C. Update function
  • D. Fit function

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the function that measures the difference between predicted and actual values in machine learning.
* Understand ML Functions:
* Optimizer function: Adjusts model parameters to minimize error (e.g., gradient descent)-it uses the cost, not defines it.
* Fit function: Trains the model by fitting it to data-process-oriented, not a measure.
* Update function: Typically updates weights during training-not a standard term for error measurement.
* Cost function: Quantifies prediction error (e.g., MSE, cross-entropy)-directly represents the difference.
* Evaluate Options:
* A: Optimizer minimizes the cost, not the cost itself-incorrect.
* B: Fit executes training, not error definition-incorrect.
* C: Update is vague and not a standard ML term for this-incorrect.
* D: Cost function (e.g., loss) measures prediction vs. target-correct.
* Reasoning: The cost function (or loss function) is the mathematical representation of error, guiding optimization.
* Conclusion: D is the correct answer.
In OCI Data Science, the documentation explains: "The cost function (or loss function) measures the difference between the model's predicted values and the actual target values, such as mean squared error for regression or cross-entropy for classification." Optimizers (A) use this to adjust weights, fit (B) is a training step, and update (C) isn't a defined function here-only the cost function (D) fits the description. This aligns with standard ML terminology and OCI's AutoML processes.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Concepts - Cost Functions".


NEW QUESTION # 152
What is a conda environment?

  • A. An environment deployment system on Oracle AI
  • B. A collection of kernels
  • C. An open-source environment management system
  • D. A system that manages package dependencies

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Conda: Conda is a widely used tool for managing packages and environments in data science.
* Evaluate Options:
* A: Partially true-Conda manages dependencies, but it's broader (an environment system).
* B: Incorrect-Kernels (e.g., Jupyter) are separate; Conda manages environments.
* C: Correct-Conda is an open-source tool for creating isolated environments with specific packages.
* D: Incorrect-Not specific to Oracle AI; it's a general tool.
* Reasoning: C captures Conda's full scope as an open-source system, beyond just dependency management (A).
* Conclusion: C is the most accurate.
OCI documentation describes Conda as "an open-source package and environment management system that allows data scientists to create isolated environments with specific versions of Python and libraries." A is too narrow, B misaligns with kernel concepts, and D ties it incorrectly to Oracle AI. C aligns with Conda's official definition and OCI's usage.
Oracle Cloud Infrastructure Data Science Documentation, "Conda Environments Overview".


NEW QUESTION # 153
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