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2Pass4sure offers Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) practice exams (desktop & web-based) which are customizable. It means candidates can set time and Oracle 1z0-1110-25 questions of the Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) practice exam according to their learning needs. The Real 1z0-1110-25 Exam environment of practice test help test takers to get awareness about the test pressure so that they become capable to counter this pressure during the final exam.
Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q145-Q150):
NEW QUESTION # 145
Which activity is NOT a part of the machine learning life cycle?
- A. Model Deployment
- B. Modeling
- C. Data Access
- D. Database Management
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify which activity isn't part of the ML lifecycle.
* Define ML Lifecycle: Includes data access, preparation, modeling, evaluation, deployment, and monitoring.
* Evaluate Options:
* A: Database Management (e.g., DBA tasks) is IT-related, not specific to ML workflows.
* B: Model Deployment (e.g., serving predictions) is a key ML phase-correctly included.
* C: Modeling (e.g., training) is the core of ML-correctly included.
* D: Data Access (e.g., retrieving data) is the first ML step-correctly included.
* Reasoning: Database management supports infrastructure, not the ML process directly.
* Conclusion: A is the outlier.
The OCI Data Science lifecycle includes "data access, exploration, feature engineering, modeling, deployment, and monitoring," per the documentation. Database Management (A) is a general ITtask (e.g., optimizing Oracle DB), not an ML-specific activity, unlike B, C, and D, which are integral to OCI's ML pipeline.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Lifecycle Overview".
NEW QUESTION # 146
You have just received a new dataset from a colleague. You want to quickly find out summary information about the dataset, such as the types of features, the total number of observations, and distributions of the data.
Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?
- A. show_corr()
- B. show_in_notebook()
- C. compute()
- D. to_xgb()
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Get summary info from an ADSDataset object.
* Evaluate Options:
* A: Correlation matrix-Specific, not full summary.
* B: Converts to XGBoost-Not for summary.
* C: Executes computation-Not summary-focused.
* D: Displays summary (types, counts, dist)-correct.
* Reasoning: show_in_notebook() provides a comprehensive overview.
* Conclusion: D is correct.
OCI documentation states: "show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook." A is partial, B and C are unrelated-only D meets the need per ADS SDK.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".
NEW QUESTION # 147
You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your networking configuration?
- A. FastConnect to an on-premises network
- B. Service Gateway with private subnet access
- C. NAT Gateway with public internet access
- D. Primary Virtual Network Interface Card (VNIC)
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Fix network timeout for pip install in a notebook.
* Evaluate Options:
* A: FastConnect-On-premises link, not public internet.
* B: VNIC-Default, not the issue.
* C: NAT Gateway-Grants internet access-correct.
* D: Service Gateway-OCI services, not PyPI.
* Reasoning: C enables outbound traffic to public repos.
* Conclusion: C is correct.
OCI documentation states: "A NAT Gateway (C) is required for notebook sessions in private subnets to access public internet repositories like PyPI." A, B, and D don't provide this-only C resolves the timeout.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Networking".
NEW QUESTION # 148
You want to use ADSTuner to tune the hyperparameters of a supported model you recently trained. You have just started your search and want to reduce the computational cost as well as assess the quality of the model class that you are using. What is the most appropriate search space strategy to choose?
- A. Perfunctory
- B. ADSTuner doesn't need a search space to tune the hyperparameters
- C. Detailed
- D. Pass a dictionary that defines a search space
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an ADSTuner strategy to minimize cost and assess model quality.
* Understand ADSTuner: Optimizes hyperparameters with configurable search spaces.
* Evaluate Options:
* A: Detailed-Exhaustive, high cost-incorrect.
* B: No search space-False; tuning requires a space.
* C: Perfunctory-Quick, low-cost assessment-correct.
* D: Dictionary-Defines space but not a strategy.
* Reasoning: Perfunctory balances cost and initial quality check.
* Conclusion: C is correct.
OCI documentation states: "ADSTuner's perfunctory strategy (C) performs a quick, low-cost search to assess model quality, ideal for initial tuning." Detailed (A) is costly, B misstates requirements, and D is a method, not a strategy-only C fits the goal.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSTuner Search Strategies".
NEW QUESTION # 149
Arrange the following in the correct Git Repository workflow order:
* Install, configure, and authenticate Git.
* Configure SSH keys for the Git repository.
* Create a local and remote Git repository.
* Commit files to the local Git repository.
* Push the commit to the remote Git repository.
- A. 2, 3, 1, 4, 5
- B. 4, 2, 3, 1, 5
- C. 3, 5, 1, 2, 4
- D. 1, 2, 3, 4, 5
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Step 1: Install, configure, and authenticate Git: Git must be installed and configured (e.g., git config -- global user.name) before any repository actions.
* Step 2: Configure SSH keys: SSH keys are set up (e.g., ssh-keygen) and added to the remote service (e.
g., GitHub, OCI Code Repository) for secure access.
* Step 3: Create local and remote Git repository: Initialize a local repo (git init) and create/link a remote repo (e.g., git remote add origin).
* Step 4: Commit files: Add files (git add .) and commit them locally (git commit -m "message").
* Step 5: Push to remote: Push local commits to the remote repo (git push origin main).
* Evaluate Options: Only D (1, 2, 3, 4, 5) follows this logical sequence; others (e.g., A starts with SSH before Git installation) are illogical.
The standard Git workflow in OCI Data Science or general practice begins with installing Git (1), configuring SSH for secure access (2), creating repositories (3), committing locally (4), and pushing remotely (5). The OCI Code Repository documentation aligns with this: "First, install Git and configure authentication (e.g., SSH), then set up repositories and manage code." D is the only option reflecting this industry-standard process.
Oracle Cloud Infrastructure Code Repository Documentation, "Git Workflow Basics".
NEW QUESTION # 150
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