CERTIFICATION 1Z0-1110-25 COST - PDF 1Z0-1110-25 CRAM EXAM

Certification 1z0-1110-25 Cost - PDF 1z0-1110-25 Cram Exam

Certification 1z0-1110-25 Cost - PDF 1z0-1110-25 Cram Exam

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Oracle 1z0-1110-25 Exam Syllabus Topics:

TopicDetails
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
  • 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 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
  • 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
  • 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 (Q112-Q117):

NEW QUESTION # 112
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. 4, 2, 3, 1, 5
  • B. 3, 5, 1, 2, 4
  • C. 2, 3, 1, 4, 5
  • 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 # 113
You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS- supported data formats?

  • A. JSON
  • B. Raw Images
  • C. Pandas DataFrame
  • D. XML
  • E. DOCX

Answer: A,C,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three data formats supported by ADS SDK for loading data.
* Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.
* Evaluate Options:
* A. DOCX: Not natively supported-requires conversion (e.g., to text).
* B. Pandas DataFrame: Supported-core format for data manipulation in ADS.
* C. JSON: Supported-common structured data format.
* D. Raw Images: Not directly supported-image data needs preprocessing (e.g., via Vision).
* E. XML: Supported-parseable structured format.
* Reasoning: ADS focuses on tabular/structured data-B, C, E align; A and D require external handling.
* Conclusion: B, C, E are correct.
OCI documentation states: "ADS SDK's DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions." DOCX (A) isn't natively handled, and raw images (D) require preprocessing outside ADS-B, C, E match the supported list.
Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".


NEW QUESTION # 114
Which technique can be used for feature engineering in the machine learning lifecycle?

  • A. K-means clustering
  • B. Support Vector Machines (SVM)
  • C. Gradient boosting
  • D. Principal Component Analysis (PCA)

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a feature engineering technique in ML.
* Understand Feature Engineering: Transforms raw data into model-ready features.
* Evaluate Options:
* A. PCA: Reduces dimensionality-feature engineering-correct.
* B. K-means: Clustering model-not feature engineering.
* C. SVM: Classification model-not feature engineering.
* D. Gradient boosting: Model training-not feature engineering.
* Reasoning: PCA creates new features via transformation-fits definition.
* Conclusion: A is correct.
OCI documentation states: "Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance." B, C, and D are modeling techniques-only A aligns with OCI's feature engineering stage.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".


NEW QUESTION # 115
You realize that your model deployment is about to reach its utilization limit. What would you do to avoid the issue before requests start to fail? Which THREE steps would you perform?

  • A. Update the deployment to use fewer instances
  • B. Update the deployment to use a larger virtual machine (more CPUs/memory)
  • C. Delete the deployment
  • D. Update the deployment to add more instances
  • E. Reduce the load balancer bandwidth limit so that fewer requests come in

Answer: B,D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Prevent deployment failure due to utilization limits.
* Understand Utilization: High load requires capacity or throttling.
* Evaluate Options:
* A: More instances-Scales horizontally-correct.
* B: Delete-Stops service, not a fix-incorrect.
* C: Fewer instances-Worsens issue-incorrect.
* D: Larger VM-Scales vertically-correct.
* E: Reduce bandwidth-Controls load-correct.
* Reasoning: A and D increase capacity, E manages demand-effective trio.
* Conclusion: A, D, E are correct.
OCI documentation states: "To avoid utilization limits, increase instances (A), use a larger compute shape (D), or reduce load balancer bandwidth (E) to manage request rates." B stops service, C reduces capacity- only A, D, E align with OCI's deployment scaling options.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".


NEW QUESTION # 116
You are working as a data scientist for a healthcare company. They decided to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended steps to develop a PySpark application in OCI Data Science?

  • A. Install a Spark conda environment, configure core-site.xml, launch a notebook session, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application
  • B. Launch a notebook session, install a PySpark conda environment, configure core-site.xml, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
  • C. Launch a notebook session, configure core-site.xml, install a PySpark conda environment, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
  • D. Configure core-site.xml, install a PySpark conda environment, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application, launch a notebook session

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence steps for a PySpark app in OCI Data Science.
* Evaluate Steps:
* Launch notebook: First-provides the environment.
* Install PySpark conda: Second-sets up Spark libraries.
* Configure core-site.xml: Third-connects to data (e.g., Object Storage).
* Develop app: Fourth-writes the PySpark code.
* Data Flow: Fifth-optional scaling, post-development.
* Check Options: D (1, 2, 3, 4, 5) matches this logical flow.
* Reasoning: Notebook first, then setup, coding, and scaling.
* Conclusion: D is correct.
OCI documentation recommends: "1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale." D follows this-others (A, B, C) misorder critical steps like launching the notebook.
Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".


NEW QUESTION # 117
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