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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Weight	Objectives
Model Development and Deployment	30%	- Model deployment 1. Prediction endpoints 2. Deployment creation - Model training 1. Hyperparameter optimization 2. Experiments
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
Machine Learning Fundamentals	20%	- Unsupervised learning 1. Clustering 2. Dimensionality reduction - Supervised learning 1. Regression 2. Classification
OCI Data Science Service	30%	- Model catalog 1. Model metadata 2. Model versioning - Projects and notebooks 1. Conda environments 2. Notebook sessions

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Oracle Cloud Infrastructure Data Science Professional 1Z0-1110-26 Prüfungsfragen mit Lösungen (Q106-Q111):

106. Frage
Where are OCI secrets stored?

A. OCI Vault
B. OCI Object Storage
C. Autonomous Data Warehouse
D. Oracle Databases

Antwort: A

Begründung:
Detailed Answer in Step-by-Step Solution:
Define OCI Secrets: Secrets are sensitive data (e.g., API keys, passwords) managed securely in OCI.
Evaluate Options:
A: Object Storage is for general data, not secure secret management.
B: Vault is OCI’s service for storing and managing secrets securely.
C: Autonomous Data Warehouse is for analytics, not secret storage.
D: Oracle Databases store data, not OCI-specific secrets.
Reasoning: Vault is purpose-built for secrets with encryption and access control.
Conclusion: B is correct.
OCI Vault “provides a secure, centralized service to store and manage secrets, such as passwords and keys, with encryption at rest and fine-grained access policies.” Object Storage (A), Autonomous Data Warehouse (C), and Oracle Databases (D) serve other purposes—only Vault (B) is designed for secrets per OCI’s security architecture.
1: Oracle Cloud Infrastructure Vault Documentation, "Secrets Management".

107. Frage
You have a dataset with fewer than 1000 observations, and you are using Oracle AutoML to build a classifier. While visualizing the results of each stage of the Oracle AutoML pipeline, you notice that no visualization has been generated for one of the stages. Which stage is not visualized?

A. Hyperparameter tuning
B. Adaptive sampling
C. Algorithm selection
D. Feature selection

Antwort: B

Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the non-visualized AutoML stage with small data.
Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.
Evaluate Options:
A: Feature selection—Visualized (e.g., feature importance).
B: Algorithm selection—Visualized (e.g., algorithm scores).
C: Adaptive sampling—Skipped/visualization absent for <1000 rows.
D: Hyperparameter tuning—Visualized (e.g., trial plots).
Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.
Conclusion: C is correct.
OCI AutoML documentation notes: “Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it’s skipped, and no visualization is generated.” Other stages (A, B,D) produce visuals—only C is absent here.
1: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".

108. Frage
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. 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
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. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel

Antwort: B

Begründung:
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".

109. Frage
Which activity is NOT a part of the machine learning life cycle?

A. Data Access
B. Modeling
C. Database Management
D. Model Deployment

Antwort: C

Begründung:
Detailed Answer in Step-by-Step Solution:
Objective: Identify which activity isn’t part of the ML lifecycle.
Define ML Lifecycle: Includes data access, preparation, modeling, evaluation, deployment, and monitoring.
Evaluate Options:
A: Database Management (e.g., DBA tasks) is IT-related, not specific to ML workflows.
B: Model Deployment (e.g., serving predictions) is a key ML phase—correctly included.
C: Modeling (e.g., training) is the core of ML—correctly included.
D: Data Access (e.g., retrieving data) is the first ML step—correctly included.
Reasoning: Database management supports infrastructure, not the ML process directly.
Conclusion: A is the outlier.
The OCI Data Science lifecycle includes “data access, exploration, feature engineering, modeling, deployment, and monitoring,” per the documentation. Database Management (A) is a general ITtask (e.g., optimizing Oracle DB), not an ML-specific activity, unlike B, C, and D, which are integral to OCI’s ML pipeline.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Lifecycle Overview".

110. Frage
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 multiple labels to an image
C. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
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

Antwort: A,B,E

111. Frage
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