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Pass Guaranteed Oracle - Fantastic 1z0-1110-25 - Exam Oracle Cloud Infrastructure 2025 Data Science Professional Topics

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>> Exam 1z0-1110-25 Topics <<

Free PDF 2025 Oracle 1z0-1110-25 –Valid Exam Topics

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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 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 (Q85-Q90):

NEW QUESTION # 85
What is a common maxim about data scientists?

A. They spend 80% of their time analyzing data and 20% finding and preparing it.
B. They spend 80% of their time finding and preparing data and 20% analyzing it.
C. They spend 80% of their time on failed analytics projects and 20% doing useful work.

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a widely accepted maxim about data scientists' time allocation.
* Understand Data Science Workflow: Involves data collection, preparation, and analysis-time distribution is key.
* Evaluate Options:
* A: 80% on finding/preparing, 20% analyzing-Reflects the data wrangling challenge.
* B: 80% analyzing, 20% finding/preparing-Inverts the common perception.
* C: 80% on failed projects, 20% useful-Pessimistic, not a standard maxim.
* Reasoning: Industry consensus (e.g., "80/20 rule") emphasizes data prep as the bulk of effort due to messy real-world data.
* Conclusion: A is correct.
OCI Data Science documentation aligns with industry norms: "Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data." B reverses this, and C isn't supported-only A reflects this widely cited maxim from sources like Forbes and OCI's practical guidance.
Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview".

NEW QUESTION # 86
You are a data scientist working for a manufacturing company. You have developed a forecasting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third-party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact. What file should be modified to include the missing libraries?

A. score.py
B. model_artifact_validate.py
C. requirements.txt
D. runtime.yaml

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Specify third-party libraries for model deployment.
* Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.
* Evaluate Options:
* A: Not a standard file-incorrect.
* B: Inference code-not for dependencies.
* C: Defines conda env with dependencies-correct.
* D: Pip list-not used in OCI conda deployments.
* Reasoning: runtime.yaml points to a conda env with all libraries.
* Conclusion: C is correct.
OCI documentation states: "In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model." score.py (B) is for logic, requirements.txt (D) isn't OCI-standard, and A doesn't exist-C fixes the issue.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".

NEW QUESTION # 87
You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?

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

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize embarrassingly parallel workload in OCI Jobs.
* Evaluate Options:
* A: Sequential runs-Inefficient for parallel tasks.
* B: Simultaneous runs-Maximizes parallelism-correct.
* C: False-Jobs support parallelism.
* D: One job per run-Misstates capability, wasteful.
* Reasoning: B leverages OCI's parallel run support.
* Conclusion: B is correct.
OCI documentation states: "For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently." A is slow, C is incorrect, and Dovercomplicates-B is the best approach.
Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".

NEW QUESTION # 88
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?

A. The NAT Gateway with public internet access
B. FastConnect to an on-premises network
C. Primary Virtual Network Interface Card (VNIC)
D. Service Gateway with private subnet access

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.
Oracle Cloud Infrastructure Data Science Documentation, "Networking for Notebook Sessions".

NEW QUESTION # 89
You are a data scientist designing an air traffic control model, and you choose to leverage Oracle AutoML.
You understand that the Oracle AutoML pipeline consists of multiple stages and automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML pipeline?

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

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence OCI AutoML pipeline stages.
* Stages:
* Adaptive sampling: Reduces data size if large.
* Feature selection: Picks relevant features.
* Algorithm selection: Chooses best model type.
* Hyperparameter tuning: Optimizes model params.
* Evaluate: C (sampling, features, algorithms, tuning) matches logical flow-data first, then model.
* Reasoning: Sampling precedes feature work-standard in OCI.
* Conclusion: C is correct.
OCI documentation states: "AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C)." Sampling reduces data first, then features and models are optimized-other orders (A, B, D) misalign with OCI's sequence.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".

NEW QUESTION # 90
......

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