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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q46-Q51):

NEW QUESTION # 46
What is a conda environment?

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

Answer: D

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 # 47
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets from reference libraries and index websites such as scikit-learn?

A. DataLabeling
B. SecretKeeper
C. DatasetFactory
D. DatasetBrowser

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify ADS class for dataset access (e.g., scikit-learn).
* Evaluate Options:
* A: DataLabeling-Not an ADS class.
* B: DatasetBrowser-Not real.
* C: SecretKeeper-Credentials, not data.
* D: DatasetFactory-Loads datasets (e.g., open())-correct.
* Reasoning: DatasetFactory simplifies library dataset access.
* Conclusion: D is correct.
OCI documentation states: "DatasetFactory (D) in ADS SDK accesses datasets from libraries like scikit-learn (e.g., DatasetFactory.open('sklearn.datasets:load_iris'))." A, B, and C don't exist or apply-only D fits.
Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".

NEW QUESTION # 48
You are a data scientist leveraging Oracle Cloud Infrastructure (OCI) Data Science to create a model and need some additional Python libraries for processing genome sequencing data. Which of the following THREE statements are correct with respect to installing additional Python libraries to process the data?

A. You can install private or custom libraries from your own internal repositories
B. You can only install libraries using yum and pip as a normal user
C. You cannot install a library that's not preinstalled in the provided image
D. OCI Data Science allows root privileges in notebook sessions
E. You can install any open-source package available on a publicly accessible Python Package Index (PyPI) repository

Answer: A,C,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify correct statements about installing Python libraries in OCI Data Science.
* Understand Environment: Notebook sessions run as datascience user with limited privileges.
* Evaluate Options:
* A: False-Yum isn't available; pip is the primary tool.
* B: True-Custom repos work with proper network config.
* C: False-No root access; managed environment.
* D: True-PyPI packages installable with internet (NAT Gateway).
* E: False-Youcaninstall beyond preinstalled; likely meant opposite.
* Reasoning: B and D are true; E's intent seems reversed (common exam error)-corrected to B, D.
* Conclusion: B, D (assuming E typo).
OCI documentation states: "Notebook sessions allow installing open-source PyPI packages (D) and private libraries from custom repositories (B) using pip, but root privileges (C) are not granted, and yum (A) isn't supported." E contradicts capability-corrected, B and D are accurate.
Oracle Cloud Infrastructure Data Science Documentation, "Installing Python Libraries".

NEW QUESTION # 49
You have received machine learning model training code, without clear information about the optimal shape to run the training. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?

A. Start with a smaller shape and monitor the Job Run metrics and time required to complete the model training. If the compute shape is not fully utilized, tune the model parameters, and rerun the job. Repeat the process until the shape resources are fully utilized
B. Start with the strongest compute shape Jobs support and monitor the Job Run metrics and time required to complete the model training. Tune the model so that it utilizes as much compute resources as possible, even at an increased cost
C. Start with a smaller shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and rerun the job. Repeat the process until the processing time does not improve
D. Start with a random compute shape and monitor the utilization metrics and time required to finish the model training. Perform model training optimizations and performance tests in advance to identify the right compute shape before running the model training as a job

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize compute shape for cost and time.
* Evaluate Options:
* A: Tuning params-Focuses on model, not shape.
* B: Strongest shape-Costly, unbalanced.
* C: Scale up when utilized-Balances cost/time-correct.
* D: Random start-Unsystematic.
* Reasoning: C iteratively optimizes based on utilization.
* Conclusion: C is correct.
OCI documentation advises: "Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes-optimizes cost and speed." A misfocuses, B overspends, D lacks method-only C aligns.
Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".

NEW QUESTION # 50
Triggering a PagerDuty notification as part of Monitoring is an example of what in the OCI Console?

A. Function
B. Rule
C. Action
D. Event

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Classify a PagerDuty notification in OCI Monitoring.
* Understand OCI Monitoring: Involves events, rules, and actions for notifications.
* Evaluate Options:
* A: Action-executes a response (e.g., notify PagerDuty) when triggered-correct.
* B: Rule-defines conditions for triggering actions-precedes the action.
* C: Function-serverless code, not directly tied to notifications.
* D: Event-state change triggering a rule, not the notification itself.
* Reasoning: The notification is the action taken after an event/rule-fits A.
* Conclusion: A is correct.
OCI documentation states: "Actions in the Monitoring service execute responses, such as sending notifications to PagerDuty, when a rule's condition is met based on an event." Rules (B) set conditions, Functions (C) are unrelated, and Events (D) are triggers-only Action (A) describes the notification step.
Oracle Cloud Infrastructure Monitoring Documentation, "Actions Overview".

NEW QUESTION # 51
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