実際的な1Z0-1110-26関連受験参考書一回合格-権威のある1Z0-1110-26関連資格試験対応

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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning Fundamentals	20%	- Supervised learning 1. Regression 2. Classification - Unsupervised learning 1. Clustering 2. Dimensionality reduction
Topic 2: OCI Data Science Service	30%	- Model catalog 1. Model versioning 2. Model metadata - Projects and notebooks 1. Notebook sessions 2. Conda environments
Topic 3: Model Development and Deployment	30%	- Model deployment 1. Deployment creation 2. Prediction endpoints - Model training 1. Hyperparameter optimization 2. Experiments
Topic 4: MLOps and OCI Integration	20%	- OCI ecosystem 1. Object Storage 2. IAM and security - Automation and pipelines 1. CI/CD integration 2. Model lifecycle management

>> 1Z0-1110-26関連受験参考書 <<

Oracle 1Z0-1110-26関連資格試験対応 & 1Z0-1110-26受験練習参考書

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Oracle Cloud Infrastructure Data Science Professional 認定 1Z0-1110-26 試験問題 (Q82-Q87):

質問 # 82
You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many sub-folders and classes. What is the best way to run this project as a Job?

A. ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file automatically
B. ZIP the entire code project folder and upload it as a Job artifact. Jobs automatically identifies the main top-level where the code is run
C. Rewrite your code so that it is a single executable Python or Bash/Shell script file
D. ZIP the entire code project folder, upload it as a Job artifact on job creation, and set JOB_RUN_ENTRYPOINT to point to the main executable file

正解：D

解説：
Detailed Answer in Step-by-Step Solution:
Objective: Run a complex Python project as an OCI Job.
Evaluate Options:
A: Auto-identification—False; entrypoint must be set.
B: Rewrite—Unnecessary, inefficient.
C: Auto-executable—False; needs explicit entrypoint.
D: ZIP with entrypoint—Correct, flexible approach.
Reasoning: D preserves structure, specifies execution.
Conclusion: D is correct.
OCI documentation states: “For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py).” Auto-detection (A, C) isn’t supported, and B discards structure—D is best.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".

質問 # 83
Which step is unique to MLOps, as opposed to DevOps?

A. Continuous delivery
B. Continuous integration
C. Continuous training
D. Continuous deployment

正解：C

解説：
Detailed Answer in Step-by-Step Solution:
Objective: Identify a step unique to MLOps vs. DevOps.
Compare MLOps and DevOps:
DevOps: Focuses on software deployment (CI/CD).
MLOps: Extends DevOps to ML, adding model-specific steps.
Evaluate Options:
A: Continuous deployment—Common to both (software/models).
B: Continuous integration—Common to both (code merging).
C: Continuous delivery—Common to both (releasing updates).
D: Continuous training—Unique to MLOps (retraining models with new data).
Reasoning: Only D addresses ML-specific needs (model retraining).
Conclusion: D is correct.
OCI documentation notes: “MLOps extends DevOps with continuous training, a process unique to machine learning where models are retrained with new data to maintain performance.” CI (B), CD (A), and delivery (C) are shared with DevOps—only continuous training (D) is MLOps-specific.
1: Oracle Cloud Infrastructure Data Science Documentation, "MLOps Concepts".

質問 # 84
Which TWO statements about Oracle Cloud Infrastructure (OCI) Open Data service are true?

A. A primary goal of Open Data is for users to contribute to the data repositories to expand the content offered.
B. Open Data is a dataset repository made for the people that create, use, and manipulate datasets.
C. Open Data includes text and image data repositories for AI and ML.
D. Audio and video formats are not available.
E. Subscribers can pay and log into Open Data to view curated datasets that are otherwise not available to the public.
F. Each dataset in Open Data consists of code and tooling usage examples for consumption and reproducibility.

正解：B、C

解説：
Detailed Answer in Step-by-Step Solution:
Analyze OCI Open Data: OCI Open Data is a free service providing access to public datasets for AI/ML use cases.
Evaluate Statements:
A: True—Open Data includes text and image datasets (e.g., geospatial images).
B: False—Video and other formats may be available depending on the dataset; no strict exclusion exists.
C: False—Datasets may include metadata, but code/tooling examples aren’t guaranteed.
D: True—It’s designed for data scientists and analysts who work with datasets.
E: False—It’s not a user-contributed repository; it’s curated by Oracle.
F: False—Open Data is free and public, not subscription-based.
Select Two: A and D align with the service’s purpose and offerings.
OCI Open Data provides access to datasets like text and images (A) for AI/ML, aimed at data professionals (D). It’s a free, curated service, not user-contributed (E) or paid (F), and while it focuses on certain formats, it doesn’t explicitly exclude audio/video (B). (Oracle Cloud Infrastructure Open Data Documentation, "Overview of Open Data").

質問 # 85
You are working as a data scientist for a healthcare company. They decide 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 stepsto develop a PySpark application in Oracle Cloud Infrastructure (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. 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
D. 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

正解：B

解説：
Detailed Answer in Step-by-Step Solution:
Objective: Sequence steps for PySpark app development.
Steps:
Launch notebook: First—sets up environment.
Install PySpark conda: Second—adds Spark libraries.
Configure core-site.xml: Third—connects to data.
Develop app: Fourth—writes code.
Data Flow: Fifth—scales (optional).
Evaluate: D (1, 2, 3, 4, 5) matches this logical order.
Reasoning: Notebook first, then setup and coding.
Conclusion: D is correct.
OCI documentation states: “1) Launch a notebook session, 2) install a PySpark conda env, 3) configure core-site.xml, 4) develop your PySpark app, 5) optionally use Data Flow (D).” Other orders (A, B, C) misplace notebook launch or config—D is correct.
1: Oracle Cloud Infrastructure Data Science Documentation, "PySpark Development".

質問 # 86
You want to build a multistep machine learning workflow by using the Oracle Cloud Infrastructure (OCI) Data Science Pipeline feature. How would you configure the conda environment to run a pipeline step?

A. Use command-line variables
B. Configure a compute shape
C. Use environmental variables
D. Configure a block volume

正解：C

解説：
Detailed Answer in Step-by-Step Solution:
Objective: Configure conda env for a pipeline step.
Evaluate Options:
A: Shape—Infra, not env config.
B: Volume—Storage, not env.
C: Command-line—Step args, not env.
D: Env variables—Sets conda path—correct.
Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).
Conclusion: D is correct.
OCI documentation states: “Configure a pipeline step’s conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition.” A, B, and C address other aspects—only D fits env config.
1: Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".

質問 # 87
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