100% PASS ORACLE - 1Z0-1110-25 - ORACLE CLOUD INFRASTRUCTURE 2025 DATA SCIENCE PROFESSIONAL USEFUL LATEST VERSION

100% Pass Oracle - 1z0-1110-25 - Oracle Cloud Infrastructure 2025 Data Science Professional Useful Latest Version

100% Pass Oracle - 1z0-1110-25 - Oracle Cloud Infrastructure 2025 Data Science Professional Useful Latest Version

Blog Article

Tags: 1z0-1110-25 Latest Version, 1z0-1110-25 Valid Examcollection, 1z0-1110-25 Discount Code, 1z0-1110-25 Question Explanations, Reliable 1z0-1110-25 Braindumps Free

Their updated Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) practice test material includes the latest and real 1z0-1110-25 questions that are very similar to those given in the actual Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) exam. Additionally, the Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) practice test software creates a realistic 1z0-1110-25 exam environment for users, and it also helps you in your preparation for the actual Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) test. Prep4sureExam offers the latest 1z0-1110-25 exam questions in multiple formats for convenience. These formats include Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) PDF dumps, 1z0-1110-25 Practice Test (web-based), and 1z0-1110-25 Practice Exam Software (Desktop-Based).

Oracle 1z0-1110-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 3
  • 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 4
  • 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 5
  • 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.

>> 1z0-1110-25 Latest Version <<

How You Can Pass the Oracle 1z0-1110-25 Exam On First Attempt

I believe that people want to have good prospects of career whatever industry they work in. Of course, there is no exception in the competitive IT industry. IT Professionals working in the IT area also want to have good opportunities for promotion of job and salary. A lot of IT professional know that Oracle Certification 1z0-1110-25 Exam can help you meet these aspirations. Prep4sureExam is a website which help you successfully pass Oracle 1z0-1110-25.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q34-Q39):

NEW QUESTION # 34
In which two ways can you improve data durability in Oracle Cloud Infrastructure Object Storage?

  • A. Enable server-side encryption
  • B. Enable Versioning
  • C. Enable client-side encryption
  • D. Limit delete permissions
  • E. Setup volumes in a RAID1 configuration

Answer: B,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two methods to enhance Object Storage durability.
* Understand Durability: Ensures data isn't lost-focus on redundancy and protection.
* Evaluate Options:
* A: RAID1-Block volume feature, not Object Storage.
* B: Encryption-Secures data, not durability.
* C: Versioning-Retains old versions, prevents loss-correct.
* D: Limit delete-Prevents accidental deletion-correct.
* E: Client encryption-Secures, not durability-focused.
* Reasoning: C and D directly protect against data loss-durability-focused.
* Conclusion: C and D are correct.
OCI documentation states: "Improve Object Storage durability with Versioning (C) to retain previous object versions and by limiting delete permissions (D) to prevent accidental loss." A isn't applicable, B and E focus on security-only C and D enhance durability per OCI's storage features.
Oracle Cloud Infrastructure Object Storage Documentation, "Data Durability Options".


NEW QUESTION # 35
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 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
  • D. 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

Answer: D

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 # 36
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. compute()
  • B. show_in_notebook()
  • C. show_corr()
  • 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 # 37
A bike sharing platform has collected user commute data for the past 3 years. For increasing profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which of the following options has the correct order of the required machine learning tasks for building a model?

  • A. Data Access, Data Exploration, Feature Exploration, Feature Engineering, Modeling
  • B. Data Access, Feature Exploration, Feature Engineering, Data Exploration, Modeling
  • C. Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling
  • D. Data Access, Feature Exploration, Data Exploration, Feature Engineering, Modeling

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform's storage system.
* Data Exploration: Once data is accessed, it's explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.
* Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.
* Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.
* Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.
Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.
The correct order reflects the machine learning lifecycle as outlined in Oracle's OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK' s correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle's recommended practices for building ML models in OCI Data Science (Reference: Oracle Cloud Infrastructure Data Science Service Documentation, "Machine Learning Lifecycle").


NEW QUESTION # 38
Which OCI service enables you to build, train, and deploy machine learning models in the cloud?

  • A. Oracle Cloud Infrastructure Data Catalog
  • B. Oracle Cloud Infrastructure Data Flow
  • C. Oracle Cloud Infrastructure Data Integration
  • D. Oracle Cloud Infrastructure Data Science

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for ML model lifecycle.
* Evaluate Options:
* A: Data Catalog-Metadata management, not ML.
* B: Data Integration-ETL, not ML.
* C: Data Science-Full ML lifecycle-correct.
* D: Data Flow-Spark processing, not full ML.
* Reasoning: C supports building, training, deploying models.
* Conclusion: C is correct.
OCI documentation states: "OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog." A, B, and D serve other purposes-only C fits the ML lifecycle per OCI's offerings.
Oracle Cloud Infrastructure Data Science Documentation, "Service Overview".


NEW QUESTION # 39
......

All the 1z0-1110-25 study materials of our company are designed by the experts and professors in the field. The quality of our study materials is guaranteed. According to the actual situation of all customers, we will make the suitable study plan for all customers. If you buy the 1z0-1110-25 Study Materials from our company, we can promise that you will get the professional training to help you pass your exam easily. By our professional training, you will pass your exam and get the related certification in the shortest time.

1z0-1110-25 Valid Examcollection: https://www.prep4sureexam.com/1z0-1110-25-dumps-torrent.html

Report this page