1z0-1110-23 Practice Exam Questions with Answers Oracle Cloud Infrastructure Data Science 2023 Professional Certification

sample()

suggeste_recoomendations()

auto_transform()

visualize_transforms()

Table extraction

Punctuation correction

Sentence diagramming

Topic classification

Sentiment analysis

Principal Component Analysis

Multiclass Classification

K-means Clustering

Recurrent Neural Network

Binary Classification

Regression Analysis

Environment Explorer

Table of Contents

Command Palette

Notebook Examples

Terminal

Corr{}

Correlation_ratio_plot{}

Pearson_plot{}

Cramersv_plot{}

Define the compute scaling strategy.

Configure the deployment infrastructure.

Define the inference server dependencies.

Execute the inference logic code

Feature Permutation Importance Explanations

Local Interpretable Model-Agnostic Explanations

Feature Dependence Explanations

Accumulated Local Effects

It is a database that stores all the features used in a machine learning model.

It helps to automate the feature engineering process.

It provides a centralized and scalable way to manage models and their metadata.

It helps to package the model and its dependencies into a lightweight, portable container.

OneHotEncoder ()

DataFrameLabelEncoder ()

show_in_notebook ()

auto_transform()

Create a new job every time you need to run your code and pass the parameters as

environment variables.

Create a new job by setting the required parameters in your code and create a new job for

every code change.

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.

Create your code to expect different parameters as command line arguments and create a

new job every time you run the code.

Create a temporary bucket on Object Storage, write all your files and data to Object Storage,

delete your notebook session, provision a new notebook session on a larger compute shape,

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and copy your files and data from your temporary bucket onto your new notebook session.

Ensure your files and environments are written to the block volume storage under the

/home/datascience directory, deactivate the notebook session, and activate the notebook

session with a larger compute shape selected.

Download all your files and data to your local machine, delete your notebook session,

provision a new notebook session on a larger compute shape, and upload your files from

your local machine to the new notebook session.

Deactivate your notebook session, provision a new notebook session on a larger compute

shape and re-create all of your file changes.

Create a pre-authenticated request (PAR) for the Object Storage bucket, and use that in the

job code.

Use the resource principal of the job run as the signer in the job code, ensuring there is a

dynamic group for this job run with appropriate access to Object Storage and the model

catalog.

Store your personal OCI config file and keys in the Vault and access the Vault through the job

run resource principal.

Package your personal OCI config file and keys in the job artifact

Create a new job with increased storage size and then run the job.

On the job run, set the environment variable that helps increase the size-of the storage.

Your code is using too much disk space. Refactor the code to identify the problem.

Edit the job, change the size of the storage of your job, and start a new job run.

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 jobs 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.

DataLabeling

DatasetBrowser

SecretKeeper

ADSTuner

Feature Selection

Adaptive Sampling

Model Deployment

Feature Extraction

Algorithm Selection

Oracle Open Data

Oracle Big Data Service

Oracle Cloud Infrastructure Data Science

Oracle Analytics Cloud

Key rotation allows you to encrypt no more than five keys at a time.

Key rotation improves encryption efficiency.

Periodically rotating keys make it easier to reuse keys.

Key rotation reduces risk if a key is ever compromised.

Periodically rotating keys limits the amount of data encrypted by one key version.

The current best-known hyperparameter configuration is returned.

A random hyperparameter configuration is returned.

A hyperparameter configuration with a minimum learning rate is returned.

The last generated hyperparameter configuration is returned