Oracle Certified 1z0-1110-25 Dumps Questions Valid 1z0-1110-25 Materials [Q67-Q82]

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Oracle Certified 1z0-1110-25 Dumps Questions Valid 1z0-1110-25 Materials

Current 1z0-1110-25 Exam Dumps [2025] Complete Oracle Exam Smoothly

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	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 3	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 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.

QUESTION 67
How can you convert a fixed load balancer to a flexible load balancer?

There is no way to convert the load balancer

Use Update Shape workflows

Delete the fixed load balancer and create a new one

Using the Edit Listener option

QUESTION 68
You have just received a new dataset from a colleague. You want to quickly find out summary information about the dataset, such as the types of features, the total number of observations, and distributions of the data.
Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?

show_corr()

to_xgb()

compute()

show_in_notebook()

QUESTION 69
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?

Mean squared error

Explained variance score

Recall

F1-score

R-squared

QUESTION 70
Which of the following analytical and statistical techniques do data scientists commonly use?

Classification

Regression

Clustering

All of the above

QUESTION 71
You are a data scientist building a pipeline in the Oracle Cloud Infrastructure (OCI) Data Science service for your machine learning project. You want to optimize the pipeline completion time by running some steps in parallel. Which statement is true about running pipeline steps in parallel?

Steps in a pipeline can be run only sequentially

Pipeline steps can be run in sequence or in parallel, as long as they create a directed acyclic graph (DAG)

All pipeline steps are always run in parallel

Parallel steps cannot be run if they are completely independent of each other

QUESTION 72
When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) DataScience model catalog, you create a score.py file. What is the purpose of the score.py file?

Configure the deployment infrastructure

Execute the inference logic code

Define the compute scaling strategy

Define the inference server dependencies

QUESTION 73
Which Web Application Firewall (WAF) service component must be configured to allow, block, or log network requests when they meet specified criteria?

Protection rules

Bot Management

Origin

Web Application Firewall policy

QUESTION 74
You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?

Feature Permutation Importance Explanations

Local Interpretable Model-Agnostic Explanations

Feature Dependence Explanations

Accumulated Local Effects

QUESTION 75
You are attempting to save a model from a notebook session to the model catalog by using ADS SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which TWO should you look for to ensure permissions are set up correctly?

The dynamic groups matching rule exists for notebook sessions in the compartment

The model artifact is saved to the block volume of the notebook session

The policy for the dynamic group grants manage permissions for the model catalog in this compartment

The networking configuration allows access to the Oracle Cloud Infrastructure services through a service gateway

The policy for your user group grants manage permissions for the model catalog in this compartment

QUESTION 76
Which cache rules criterion matches if the concatenation of the requested URL path and query are identical to the contents of the value field?

URL_PART_CONTAINS

URL_IS

URL_PART_ENDS_WITH

URL_STARTS_WITH

QUESTION 77
You are working as a Data Scientist for a healthcare company. You have a series of neurophysiological data on OCI Data Science and have developed a convolutional neural network (CNN) classification model. It predicts the source of seizures in drug-resistant epileptic patients. You created a model artifact with all the necessary files. When you deployed the model, it failed to run because you did not point to the correct conda environment in the model artifact. Where would you provide instructions to use the correct conda environment?

score.py

runtime.yaml

requirements.txt

model_artifact_validate.py

QUESTION 78
What is feature engineering in machine learning used for?

To perform parameter tuning

To interpret ML models

To transform existing features into new ones

To help understand the dataset features

QUESTION 79
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?

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

Create the job in Data Science Jobs and then start the number of simultaneous job runs required for your workload

Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel workloads

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

QUESTION 80
Which OCI service provides a scalable environment for developers and data scientists to run Apache Spark applications at scale?

Data Science

Anomaly Detection

Data Labeling

Data Flow

QUESTION 81
Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?

Model Catalog

Jobs

Notebook Sessions

Projects

QUESTION 82
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets from reference libraries and index websites such as scikit-learn?

DataLabeling

DatasetBrowser

SecretKeeper

DatasetFactory