Blog posts - Procurement, Contracts & Entrepreneurship lessons!

Paul Cook Paul Cook

0 Course Enrolled • 0 Course Completed

Biography

Valid 1z0-1110-25 Test Practice & New APP 1z0-1110-25 Simulations

We offer you free update for one year after purchasing, that is to say, in the following year, you will get the updated version for 1z0-1110-25 learning materials for free. And our system will immediately send the latest version to your email address automatically once they update. What’s more, the 1z0-1110-25 Learning Materials are high quality, and it will ensure you to pass the exam successfully. Pass guarantee and money back guarantee if you can’t pass the exam.

Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 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.

>> Valid 1z0-1110-25 Test Practice <<

New APP 1z0-1110-25 Simulations | 1z0-1110-25 Exam Price

All knowledge contained in our 1z0-1110-25 Practice Engine is correct. Our workers have checked for many times. Also, we will accept annual inspection of our 1z0-1110-25 exam simulation from authority. The results show that our 1z0-1110-25 study materials completely have no problem. Our company is rated as outstanding enterprise. And at the same time, our website have became a famous brand in the market. We also find that a lot of the fake websites are imitating our website, so you have to be careful.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q28-Q33):

NEW QUESTION # 28
As a data scientist, you create models for cancer prediction based on mammographic images. The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix. Which model would you prefer and why?
* Model 1 has Test accuracy is 80% and recall is 70%
* Model 2 has Test accuracy is 75% and recall is 85%

A. Model 2, because recall has more impact on predictions in this use case
B. Model 1, because the test accuracy is high
C. Model 1, because recall has lesser impact on predictions in this use case
D. Model 2, because recall is high

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Choose the better model for cancer prediction based on metrics.
* Understand Metrics:
* Accuracy: Overall correct predictions.
* Recall: True positives / (True positives + False negatives)-crucial for cancer (minimizing misses).
* Context: Cancer prediction prioritizes recall-false negatives (missed cancers) are critical.
* Evaluate Models:
* Model 1: 80% accuracy, 70% recall-Misses more cancers.
* Model 2: 75% accuracy, 85% recall-Misses fewer cancers.
* Evaluate Options:
* A: High recall-True, but lacks context.
* B: High accuracy-Misses recall's importance.
* C: Recall's impact-Correct for cancer use case-best.
* D: Lesser recall impact-Incorrect for this priority.
* Reasoning: C emphasizes recall's critical role-aligns with medical needs.
* Conclusion: C is correct.
OCI documentation advises: "For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at
85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C)." A is partial, B overlooks context, D reverses priority-only C fits OCI's ML evaluation guidance for this scenario.
Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".

NEW QUESTION # 29
Where do calls to stdout and stderr from score.py go in a model deployment?

A. The file that was defined for them on the Virtual Machine (VM)
B. The OCI console
C. The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment
D. The OCI Cloud Shell, which can be accessed from the console

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate score.py output in OCI model deployment.
* Understand Deployment: Logs are centralized in OCI Logging.
* Evaluate Options:
* A: VM file-Not default; requires custom config-incorrect.
* B: Predict log in OCI Logging-Standard destination-correct.
* C: Cloud Shell-Separate tool, not logs-incorrect.
* D: Console-UI, not raw logs-incorrect.
* Reasoning: B aligns with OCI's logging integration.
* Conclusion: B is correct.
OCI documentation states: "score.py stdout and stderr are captured in the predict log within OCI Logging service (B), configured during deployment." A isn't standard, C and D don't receive logs-only B fits OCI's logging setup.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Logging".

NEW QUESTION # 30
As you are working in your notebook session, you find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?

A. Ensure your files and environments are written to the block volume storage under the /home
/datascience directory, deactivate the notebook session, and activate the notebook session with a larger compute shape selected
B. Deactivate your notebook session, provision a new notebook session on a larger compute shapeand re- create all of your file changes
C. Create a temporary bucket on Object Storage, write all your files and data to Object Storage, delete your notebook session, provision a new notebook session on a larger compute shape, and copy your files and data from your temporary bucket onto your new notebook session
D. Download all your files and data to your local machine, delete your notebook session, provision a new notebook session on a larger compute shape, and upload your files from your local machine to the new notebook session

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Scale up notebook without losing work.
* Evaluate Options:
* A: Object Storage-Extra steps, inefficient.
* B: Block volume-Persists data, seamless scale-correct.
* C: Local machine-Risky, cumbersome.
* D: Recreate-Loses work, impractical.
* Reasoning: B uses OCI's built-in persistence.
* Conclusion: B is correct.
OCI documentation states: "Files in /home/datascience (B) persist on block volume; deactivate, then reactivate with a larger shape to scale up without data loss." A, C, and D add complexity or risk-only B is optimal per OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".

NEW QUESTION # 31
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which of the following TWO prevailing metrics would you use for evaluating a multiclass classification model?

A. Explained variance score
B. Recall
C. R-Squared
D. F1 Score
E. Mean squared error

Answer: B,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select two metrics for multiclass classification in AutoML.
* Understand Multiclass Metrics: Focus on class-specific performance-classification, not regression.
* Evaluate Options:
* A. Recall: Measures true positives per class-key for multiclass-correct.
* B. Mean squared error: Regression metric-incorrect.
* C. F1 Score: Balances precision and recall-standard for multiclass-correct.
* D. R-Squared: Regression fit-incorrect.
* E. Explained variance: Regression metric-incorrect.
* Reasoning: A and C assess classification accuracy across multiple classes-fit AutoML's evaluation.
* Conclusion: A and C are correct.
OCI AutoML documentation states: "For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance." B, D, and E are regression- focused-only A and C are supported and relevant per OCI's AutoML metrics suite.
Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".

NEW QUESTION # 32
You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces could you use to save the model artifact into the model catalog?

A. Console
B. Accelerated Data Science (ADS) Software Development Kit (SDK)
C. Git CLI
D. ODSC CLI
E. Oracle Cloud Infrastructure (OCI) Command Line Interface (CLI)
F. OCI Python SDK

Answer: A,B,F

NEW QUESTION # 33
......

The price for 1z0-1110-25 training materials is quite reasonable, and no matter you are a student or you are an employee at school, you can afford it. 1z0-1110-25 exam dumps are edited by experienced experts, therefore the quality can be guaranteed. 1z0-1110-25 training materials contain both questions and answers, and it’s convenient for you to check the answers after finish practicing. In addition, 1z0-1110-25 Exam Dumps cover most knowledge points of the exam, and you can also improve your ability in the process of learning.

New APP 1z0-1110-25 Simulations: https://www.premiumvcedump.com/Oracle/valid-1z0-1110-25-premium-vce-exam-dumps.html