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To get prepared for the Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) certification exam, applicants face a lot of trouble if the study material is not updated. They are using outdated materials resulting in failure and loss of money and time. So to solve all these problems, Exam4Tests offers actual 1z0-1110-25 Questions to help candidates overcome all the obstacles and difficulties they face during 1z0-1110-25 examination preparation.

Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
Topic 1	Implement End-to-End Machine Learning Lifecycle: This section evaluates the abilities of Machine Learning Engineers and includes an end-to-end walkthrough of the ML lifecycle within OCI. It involves data acquisition from various sources, data preparation, visualization, profiling, model building with open-source libraries, Oracle AutoML, model evaluation, interpretability with global and local explanations, and deployment using the model catalog.
Topic 2	Use Related OCI Services: This final section measures the competence of Machine Learning Engineers in utilizing OCI-integrated services to enhance data science capabilities. It includes creating Spark applications through OCI Data Flow, utilizing the OCI Open Data Service, and integrating other tools to optimize data handling and model execution workflows.
Topic 3	OCI Data Science - Introduction & Configuration: This section of the exam measures the skills of Machine Learning Engineers and covers foundational concepts of Oracle Cloud Infrastructure (OCI) Data Science. It includes an overview of the platform, its architecture, and the capabilities offered by the Accelerated Data Science (ADS) SDK. It also addresses the initial configuration of tenancy and workspace setup to begin data science operations in OCI.
Topic 4	Apply MLOps Practices: This domain targets the skills of Cloud Data Scientists and focuses on applying MLOps within the OCI ecosystem. It covers the architecture of OCI MLOps, managing custom jobs, leveraging autoscaling for deployed models, monitoring, logging, and automating ML workflows using pipelines to ensure scalable and production-ready deployments.
Topic 5	Create and Manage Projects and Notebook Sessions: This part assesses the skills of Cloud Data Scientists and focuses on setting up and managing projects and notebook sessions within OCI Data Science. It also covers managing Conda environments, integrating OCI Vault for credentials, using Git-based repositories for source code control, and organizing your development environment to support streamlined collaboration and reproducibility.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q49-Q54):

NEW QUESTION # 49
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. 1, 2, 3, 4, 5
B. 2, 3, 1, 4, 5
C. 3, 5, 1, 2, 4
D. 4, 2, 3, 1, 5

Answer: A

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 # 50
You have created a conda environment in your notebook session. This is the first time you are working with published conda environments. You have also created an Object Storage bucket with permission to manage the bucket. Which TWO commands are required to publish the conda environment?

A. odsc conda init --bucket_namespace <NAMESPACE> --bucket_name <BUCKET>
B. odsc conda publish --slug <SLUG>
C. conda activate /home/datascience/conda/<SLUG>
D. odsc conda create --file manifest.yaml
E. odsc conda list --override

Answer: A,B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Publish a conda env to Object Storage.
* Process: Initialize bucket config, then publish env.
* Evaluate Options:
* A: Publishes env with slug-correct final step.
* B: Lists envs-unrelated to publishing.
* C: Sets bucket details-required setup-correct.
* D: Creates env-not publishing.
* E: Activates env-not for sharing.
* Reasoning: C sets up, A executes-standard workflow.
* Conclusion: A and C are correct.
OCI documentation states: "To publish a conda environment, first run odsc conda init (C) with bucket namespace and name, then odsc conda publish (A) with a slug to upload to Object Storage." B, D, and E serve other purposes-only A and C are required per OCI's process.
Oracle Cloud Infrastructure Data Science CLI Reference, "Publishing Conda Environments".

NEW QUESTION # 51
You have an embarrassingly parallel or distributed batch job with a large amount of data 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. Create a new job for every job run that you have to run in parallel, because the Data Science Job service can have only one job per job
C. Create a job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
D. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize an embarrassingly parallel job in OCI Data Science Jobs.
* Define Embarrassingly Parallel: Tasks are independent, ideal for simultaneous runs.
* Evaluate Options:
* A: Multiple simultaneous runs-Leverages parallelism-correct.
* B: One job per run-Misstates capability; unnecessary complexity.
* C: Sequential runs-Inefficient, ignores parallelism.
* D: False-Jobs support parallelism.
* Reasoning: A maximizes efficiency for parallel tasks.
* Conclusion: A is correct.
OCI documentation states: "For embarrassingly parallel workloads, create a single Job and launch multiple simultaneous Job Runs to process data in parallel." B misinterprets limits, C wastes time, and D denies capability-only A fits OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Runs".

NEW QUESTION # 52
Six months ago you created and deployed a model that predicts customer churn for a call center. Initially, it was yielding quality predictions. However, over the last two months, users have been questioning the credibility of the predictions. Which TWO methods would you employ to verify accuracy and lower customer churn?

A. Operational monitoring
B. Validate the model using recent data
C. Retrain the model
D. Drift monitoring
E. Redeploy the model

Answer: C,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Address declining model performance and improve churn prediction.
* Analyze Issue: Poor predictions suggest data drift or model staleness-common ML challenges.
* Evaluate Options:
* A. Drift monitoring: Tracks changes in data distribution-identifies root cause of accuracy drop- correct.
* B. Redeploy the model: Repeats deployment without fixing the issue-ineffective alone.
* C. Operational monitoring: Tracks system health (e.g., latency), not prediction quality.
* D. Retrain the model: Updates model with new data-directly improves accuracy-correct.
* E. Validate with recent data: Checks performance but doesn't fix-diagnostic, not corrective.
* Reasoning: A diagnoses drift (cause), D retrains to adapt (solution)-best combo to verify and lower churn.
* Conclusion: A and D are correct.
OCI documentation advises: "Drift monitoring (A) detects shifts in data distribution that degrade model performance, while retraining (D) with fresh data restores accuracy." Redeployment (B) doesn't address drift, operational monitoring (C) focuses on infra, and validation (E) is a check-not a fix. A and D align with OCI' s model maintenance strategy.
Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".

NEW QUESTION # 53
Using Oracle AutoML, you are tuning hyperparameters on a supported model class and have specified a time budget. AutoML terminates computation once the time budget is exhausted. What would you expect AutoML to return in case the time budget is exhausted before hyperparameter tuning is completed?

A. The current best-known hyperparameter configuration
B. A hyperparameter configuration with a minimum learning rate
C. A random hyperparameter configuration
D. The last generated hyperparameter configuration

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Predict AutoML's output when time runs out during tuning.
* Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.
* Evaluate Options:
* A: Best-known config-Logical, reflects optimization goal-correct.
* B: Last config-Ignores prior better results-incorrect.
* C: Minimum learning rate-Arbitrary, not performance-based.
* D: Random-Defeats tuning purpose.
* Reasoning: AutoML prioritizes the best config found within the budget.
* Conclusion: A is correct.
OCI AutoML documentation states: "If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics." Last (B), minimum (C), or random (D) configs aren't selected-only A aligns with OCI's optimization strategy.
Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".

NEW QUESTION # 54
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1Z0-1110-25 AUTHORIZED PDF - NEW 1Z0-1110-25 TEST CRAM

1z0-1110-25 Authorized Pdf - New 1z0-1110-25 Test Cram