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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning Fundamentals	20%	- Supervised learning 1. Classification 2. Regression - Unsupervised learning 1. Clustering 2. Dimensionality reduction
Topic 2: OCI Data Science Service	30%	- Model catalog 1. Model metadata 2. Model versioning - 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. Experiments 2. Hyperparameter optimization
Topic 4: MLOps and OCI Integration	20%	- OCI ecosystem 1. Object Storage 2. IAM and security - Automation and pipelines 1. Model lifecycle management 2. CI/CD integration

>> 1Z0-1110-26시험패스 인증공부자료 <<

Oracle 1Z0-1110-26인기자격증 덤프공부자료 & 1Z0-1110-26높은 통과율 인기 시험자료

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최신 Oracle Cloud 1Z0-1110-26 무료샘플문제 (Q91-Q96):

질문 # 91
Which two statements are true about published conda environments?

A. Your notebook session acts as the source to share published conda environments with team members
B. The odsc conda init command is used to configure the location of published conda environments
C. They are curated by Oracle Cloud Infrastructure (OCI) Data Science
D. You can only create a published conda environment by modifying a Data Science conda environment
E. In addition to service job run environment variables, conda environment variables can be used inData Science Jobs

정답：B,E

설명：
Detailed Answer in Step-by-Step Solution:
Objective: Identify true statements about published conda environments in OCI.
Understand Published Conda: Custom envs shared via Object Storage.
Evaluate Options:
A: False—Users create them, not OCI-curated.
B: True—odsc conda init sets up access to published envs.
C: False—Object Storage, not notebook, is the source.
D: False—Can create from scratch, not just modifications.
E: True—Conda env vars extend job configs.
Reasoning: B configures access, E enhances flexibility—match OCI behavior.
Conclusion: B and E are correct.
OCI documentation states: “odsc conda init (B) configures the bucket for published conda environments, and Jobs can use conda environment variables (E) alongside service vars.” A misattributes curation, C misplaces source, and D limits creation—only B and E are true per OCI’s conda management.
1: Oracle Cloud Infrastructure Data Science Documentation, "Published Conda Environments".

질문 # 92
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?

A. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel
B. Create a job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
C. Create a new job for every job run that you have to run in parallel, because the Data Science Job service can have only one job per job
D. Create the job in Data Science Jobs and start a job run. When it is done, start a new job run until you achieve the number of runs required

정답：B

설명：
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".

질문 # 93
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?

A. Create a new job by setting the required parameters in your code, and create a new job for every code change
B. Create a new job every time you need to run your code and pass the parameters as environment variables
C. Create your code to expect different parameters as command-line arguments, and create a new job every time you run the code
D. Create your code to expect different parameters either as environment variables or as command-line arguments, which are set on every job run with different values

정답：D

설명：
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs.
Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args).
Evaluate Options:
A: New job per run with env vars—Redundant job creation, inefficient.
B: New job per run with args—Similarly inefficient due to repeated setup.
C: Hardcode params, new job per change—Highly inefficient, requires code edits.
D: Single job, flexible params via env vars or args—Efficient, reusable—correct.
Reasoning: D minimizes job creation, allows runtime flexibility via configuration—best practice.
Conclusion: D is correct.
OCI documentation states: “For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK—most efficient approach.” Options A, B, and C involve unnecessary job proliferation or code changes—only D aligns with OCI’s design for parameterized runs.
1: Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".

질문 # 94
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, upload it as a Job artifact on job creation, and set JOB_RUN_ENTRYPOINT to point to the main executable file
C. 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
D. Rewrite your code so that it is a single executable Python or Bash/Shell script file

정답：B

설명：
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".

질문 # 95
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image dat a. Which of the following THREE are possible ways to annotate an image in Data Labeling?

A. Adding a single label to an image
B. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
C. Adding multiple labels to an image
D. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image
E. Adding labels to an image using object detection, by drawing bounding boxes to an image

정답：A,C,E

질문 # 96
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