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| Section | Objectives |
|---|---|
| Topic 1: Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools |
| Topic 2: Apply MLOps Practices | - Implement model lifecycle management - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models |
| Topic 3: OCI Data Science - Introduction and Configuration | - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions |
| Topic 4: Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog - Prepare and manage datasets |
| Topic 5: Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services - Design machine learning solutions for business use cases |
>> 1Z0-1110-26 Original Questions <<
The BraindumpQuiz is a leading platform that has been assisting the Oracle 1Z0-1110-26 exam candidates for many years. Over this long time period countless 1Z0-1110-26 exam candidates have passed their Oracle 1Z0-1110-26 Exam. They got success in Oracle Cloud Infrastructure Data Science Professional exam with flying colors and did a job in top world companies.
NEW QUESTION # 116
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?
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Runs".
NEW QUESTION # 117
Which OCI Data Science interaction method can function without the need of scripting?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI Data Science interaction method that doesn’t require scripting.
Understand Interaction Methods: OCI provides multiple ways to interact with Data Science services—some are GUI-based, others script-based.
Evaluate Options:
A . OCI Console: A web-based graphical interface allowing users to manage resources (e.g., create notebook sessions, deploy models) via point-and-click—no scripting needed.
B . CLI: Command Line Interface requires writing commands (scripts) to execute tasks (e.g., oci data-science notebook-session create).
C . Language SDKs: Software Development Kits (e.g., Python SDK) require coding to interact programmatically (e.g., oci.data_science.DataScienceClient).
D . REST APIs: Application Programming Interfaces require scripted HTTP requests (e.g., using curl or a programming language).
Reasoning: Only the OCI Console (A) offers a no-code, user-friendly interface, while B, C, and D rely on scripting or programming.
Conclusion: A is the correct answer as it eliminates the need for scripting.
The OCI Console is described in the documentation as “a browser-based interface that allows users to manage OCI Data Science resources, such as creating notebook sessions or jobs, without writing code or scripts.” In contrast, the CLI (B) requires command-line scripts, SDKs (C) need programming (e.g., Python), and REST APIs (D) involve scripted API calls. The Console’s GUI distinguishes it as the only option functioning without scripting, aligning with Oracle’s design for accessibility to non-programmers.
1: Oracle Cloud Infrastructure Data Science Documentation, "Getting Started with OCI Console" section.
NEW QUESTION # 118
You are a data scientist leveraging the Oracle Cloud Infrastructure (OCI) Language AI service for various types of text analyses. Which TWO capabilities can you utilize with this tool?
Answer: D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify two OCI Language AI capabilities.
Understand OCI Language: Focuses on text analysis tasks.
Evaluate Options:
A: Table extraction—Vision, not Language—incorrect.
B: Punctuation correction—Not offered—incorrect.
C: Sentence diagramming—Not supported—incorrect.
D: Topic classification—Supported (custom/pretrained)—correct.
E: Sentiment analysis—Supported (pretrained)—correct.
Reasoning: D and E are core text analysis features of OCI Language.
Conclusion: D and E are correct.
OCI documentation states: “OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features.” A belongs to Vision, B and C aren’t available—only D and E match OCI Language’s capabilities.
1: Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".
NEW QUESTION # 119
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 network configuration?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Diagnose a network timeout during pip install in a notebook session.
Understand Notebook Networking: Sessions run in a VCN; internet access requires specific configs.
Analyze Timeout: Indicates failure to reach the public PyPI repository—likely no internet outbound route.
Evaluate Options:
A: NAT Gateway—Provides internet access for private subnets—correct fix.
B: Service Gateway—Accesses OCI services privately, not public internet.
C: FastConnect—Links to on-premises, not public internet.
D: VNIC—Essential but present by default; doesn’t solve internet access.
Reasoning: NAT Gateway enables outbound traffic to public repos like PyPI.
Conclusion: A is correct.
OCI documentation notes: “Notebook sessions in a private subnet require a NAT Gateway to access public internet resources, such as PyPI, for package installation via pip. Without it, network timeouts occur.” Service Gateway (B) is for OCI services, FastConnect (C) is irrelevant, and VNIC (D) is standard—only A resolves the issue.
1: Oracle Cloud Infrastructure Data Science Documentation, "Networking for Notebook Sessions".
NEW QUESTION # 120
Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the interactive coding environment in OCI Data Science.
Evaluate Options:
A: Model Catalog stores models—not for coding.
B: Jobs run predefined tasks—not interactive.
C: Notebook Sessions provide JupyterLab for coding and training—interactive.
D: Projects organize work—not a coding environment.
Reasoning: Notebook Sessions are OCI’s Jupyter-based tool for interactive ML development.
Conclusion: C is correct.
OCI Data Science Notebook Sessions “provide an interactive JupyterLab environment where data scientists can write code, explore data, and train machine learning models.” Model Catalog (A) is for storage, Jobs (B) for automation, and Projects (D) for organization—only C offers interactivity.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Sessions Overview".
NEW QUESTION # 121
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