1Z0-1110-26 Exam Practice Questions prepared by Oracle Professionals [Q94-Q118]

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1Z0-1110-26 Exam Practice Questions prepared by Oracle Professionals

Use Valid New 1Z0-1110-26 Questions – Top choice Help You Gain Success

Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Weight	Objectives
OCI Data Science – Introduction & Configuration	10%	– Tenancy and environment configuration for Data Science – Overview and core concepts of OCI Data Science – Capabilities of the Accelerated Data Science (ADS) SDK
Design and Set Up Data Science Workspace	15%	– Manage access control, security, and IAM integration – Configure compute shapes, storage, and networking – Create and manage projects and notebook sessions
Integrate Related OCI Services	10%	– Use OCI AI and data services with Data Science – Integration with OCI Object Storage, Vault, and Networking
Apply MLOps Practices	20%	– ML pipelines, automation, and reproducibility – Governance, auditing, and compliance – Model monitoring, drift detection, and performance tracking
Implement End-to-End Machine Learning Lifecycle	45%	– Model saving, cataloging, and versioning – Data preparation, exploration, and transformation – Deploy models and manage endpoints – Model development, training, and evaluation – Use AutoML and built-in algorithms

NEW QUESTION 94
You are a data scientist; you use the Oracle Cloud Infrastructure (OCI) Language service to train custom models. Which types of custom models can be trained?

Image classification, Named Entity Recognition (NER)

Text classification, Named Entity Recognition (NER)

Sentiment Analysis, Named Entity Recognition (NER)

Object detection, Text classification

NEW QUESTION 95
Which OCI service provides a managed Kubernetes service for deploying, scaling, and managing containerized applications?

Oracle Cloud Infrastructure Container Registry

Oracle Cloud Infrastructure Load Balancing

Oracle Cloud Infrastructure Container Engine for Kubernetes

Oracle Cloud Infrastructure Streaming

NEW QUESTION 96
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?

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

Rewrite your code so that it is a single executable Python or Bash/Shell script file

ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file automatically

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

NEW QUESTION 97
You have just started as a data scientist at a healthcare company. You have been asked to analyze and improve a deep neural network model, which was built based on the electrocardiogram records of patients. There are no details about the model framework that was built. What would be the best way to find more details about the machine learning models inside the model catalog?

Refer to the code inside the model

Check for model taxonomy details

Check for metadata tags

Check for provenance details

Detailed Answer in Step-by-Step Solution:
Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.
Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.
Evaluate Options:
A . Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn’t typically include readable source code; it’s a trained object, not the training script.
B . Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.
C . Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).
D . Check for provenance details: Provenance tracks the model’s creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.
Reasoning: Provenance details are designed to document the “how” and “what” of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.
Conclusion: D is the best approach for detailed investigation.
In OCI Data Science, the Model Catalog stores provenance information, which includes “details about the model’s origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references.” This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog – Provenance Details" section.

NEW QUESTION 98
Which activity is NOT a part of the machine learning life cycle?

Database Management

Model Deployment

Modeling

Data Access

NEW QUESTION 99
What is a conda environment?

A system that manages package dependencies

A collection of kernels

An open-source environment management system

An environment deployment system on Oracle AI

NEW QUESTION 100
How can you collaborate with team members in OCI Data Science Workspace?

By granting access to specific notebooks and files

By using version control systems integrated with the workspace

By sharing the workspace instance with other users

By enabling chat and video conferencing within the workspace

NEW QUESTION 101
What is the name of the machine learning library used in Apache Spark?

MLib

GraphX

Structured Streaming

HadoopML

NEW QUESTION 102
What is the first step in the data science process?

Collecting data and preparing it for analysis

Experimenting with and tuning different analytical models

Defining an analytical hypothesis that could provide business value

Working with data owners

NEW QUESTION 103
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

NEW QUESTION 104
You want to make your model more frugal to reduce the cost of collecting and processing dat a. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method is appropriate to display the comparability between Continuous and Categorical features?

pearson_plot()

cramersv_plot()

correlation_ratio_plot()

corr()

NEW QUESTION 105
The feature type TechJob has the following registered validators:
TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_default_handler) TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_open_handler, condition=('job_family',)) TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_closed_handler, condition=('job_family': 'IT'))When you run is_tech_job(job_family='Engineering'), what does the feature type validator system do?

Execute the is_tech_job_default_handler handler

Throw an error because the system cannot determine which handler to run

Execute the is_tech_job_closed_handler handler

Execute the is_tech_job_open_handler handler

NEW QUESTION 106
You have an image classification model in the model catalog which is deployed as an HTTP endpoint using model deployments. Your tenancy administrator is seeing increased demands and has asked you to increase the load balancing bandwidth from the default of 10Mbps. You are provided with the following information:
Payload size in KB = 1024
Estimated requests per second = 120 requests/second (Monday through Friday, in every month, in every year) Buffer percentage = 20%What is the optimal load balancing bandwidth to redeploy your model?

452 Mbps

52 Mbps

7052 Mbps

1152 Mbps

NEW QUESTION 107
You want to make your model more parsimonious to reduce the cost of collecting and processing dat a. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method would be appropriate to display the correlation between Continuous and Categorical features?

corr()

correlation_ratio_plot()

pearson_plot()

cramersv_plot()

NEW QUESTION 108
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?

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

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

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

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

NEW QUESTION 109
Which statement about logs for Oracle Cloud Infrastructure Jobs is true?

Each job run sends outputs to a single log for that job

Integrating data science jobs resources with logging is mandatory

All stdout and stderr are automatically stored when automatic log creation is enabled

Logs are automatically deleted when the job and job run is deleted

NEW QUESTION 110
You have trained three different models on your dataset using Oracle AutoML. You want to visualize the behavior of each of the models, including the baseline model, on the test set. Which class should be used from the Accelerated Data Science (ADS) SDK to visually compare the models?

EvaluationMetrics

ADSEvaluator

ADSExplainer

ADSTuner

NEW QUESTION 111
How are datasets exported in the OCI Data Labeling service?

As a binary file

As an XML file

As a line-delimited JSON file

As a CSV file

NEW QUESTION 112
You have built a machine model to predict whether a bank customer is going to default on a loan. You want to use Local Interpretable Model-Agnostic Explanations (LIME) to understand a specific prediction. What is the key idea behind LIME?

Global behaviour of a machine learning model may be complex, while the local behaviour may be approximated with a simpler surrogate model

Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models

Global and local behaviours of machine learning models are similar

Local explanation techniques are model-agnostic, while global explanation techniques are not

NEW QUESTION 113
Where do calls to stdout and stderr from score.py go in a model deployment?

The file that was defined for them on the Virtual Machine (VM)

The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment

The OCI Cloud Shell, which can be accessed from the console

The OCI console

NEW QUESTION 114
Which of the following best describes the principal goal of data science?

To collect and archive exhaustive datasets from various source systems for corporate record-keeping uses.

To mine and analyze large amounts of data in order to uncover information that can be used for operational improvements and business gains.

To collect and prepare data for use as part of analytics applications.

Data science is focused on output of the analysis.

NEW QUESTION 115
Which components are a part of the OCI Identity and Access Management service?

Policies

Regional subnets

Compute instances

VCN

NEW QUESTION 116
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

NEW QUESTION 117
You loaded data into Oracle Cloud Infrastructure (OCI) Data Science. To transform the data, you want to use the Accelerated Data Science (ADS) SDK. When you applied the get_recommendations() tool to the ADSDataset object, it showed you user-detected issues with all the recommended changes to apply to the dataset. Which option should you use to apply all the recommended transformations at once?

get_transformed_dataset()

fit_transform()

auto_transform()

visualize_transforms()

NEW QUESTION 118
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