1z0-1110-23 Oracle Cloud Infrastructure Data Science 2023 Professional Question and Answers

Package your personal OC file and keys in the job artifact.

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 kays in the Vault, and access the Vault through the job nun resource principal

Create a pre-authenticated request (PAA) for the Object Storage bucket, and use that in the job code.

Data Integration

Vault

Data Flow

Analytics Cloud

OCI Translation

OCI Language

OCI Anomaly Detection

OCI Speech

Retrain the model

Validate the model using recent data

Drift monitoring

Redeploy the model

Operational monitoring

Detailed

ADSTuner doesn't need a search space to tune the hyperparameters.

Perfunctory

Pass a dictionary that defines a search space

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.

You can only install libraries using yum and pip as a normal user.

You can install private or custom libraries from your own internal repositories.

OCI Data Science allows root privileges in notebook sessions.

You can install any open source package available on a publicly accessible Python Package

Index (PyPI) repository.

You cannot install a library that's not preinstalled in the provided image

Update the deployment to use fewer instances.

Delete the deployment.

Reduce the load balancer bandwidth limit so that fewer requests come in.

Update the deployment to use a larger virtual machine (mare CPUs/memory).

Update the deployment to add more instances.

Oracle Autonomous Data Warehouse

Oracle Machine Learning (OML)

Oracle Big Data Service

Conda environments in Oracle Cloud Infrastructure (OCI) Data Science

Python Package Index (PyPI

visualize_transforms ()

auto_transform()

sample ()

suggest_recommendations()

show_corr()

to_xgb ()

compute ()

show_in_notebook ()

sample()

suggeste_recoomendations()

auto_transform()

visualize_transforms()

Explained Variance Score

F-1 Score

Weighted Precision

Weighted Recall

Mean Absolute Error

model_artifact_validate.py

score.py

requirements.txt

runtime.yaml

On job creation, enable logging and select a log group. Then, select either a log or the option

to enable automatic log creation.

Make sure that your code is using the standard logging library and then store all the logs to

Object Storage at the end of the job.

Create your own log group and use a third-party logging service to capture job run details for

log collection and storing.

You can implement custom logging in your code by using the Data Science Jobs logging

service.

Launch a notebook session. Install a PySpark conda environment. Configure core-site.xml.

Develop your PySpark application. Create a Data Flow application with the Accelerated Data

Science (ADS) SDK.

Install a Spark conda environment. Configure core-site.xml. Launch a notebook session.

Create a Data Flow application with the Accelerated Data Science (ADS) SDK. Develop your

PySpark application.

Configure core-site.xml. Install a PySpark conda environment. Create a Data Flow application

with the Accelerated Data Science (ADS) SDK. Develop your PySpark application. Launch a

notebook session.

Launch a notebook session. Configure core-site.xml. Install a PySpark conda environment.

Develop your PySpark application. Create a Data Flow application with the Accelerated Data

Science (ADS) SDK.

Moving a project to a different compartment also moves its associated notebook sessions

and models to the new compartment.

Moving a project to a different compartment requires deleting all its associated notebook

sessions and models first.

You cannot move a project to a different compartment after it has been created.

You can move a project to a different compartment without affecting its associated

notebook sessions and models

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.

Environment Explorer

Table of Contents

Command Palette

Notebook Examples

Terminal

Start with a random compute shape and monitor the utilization metrics and time required to

finish the model training. Perform model training optimizations and performance tests in

advance to identify the right compute shape before running the model training as a job.

Start with a smaller shape and monitor the Job Run metrics and time required to complete

the model training. If the compute shape is not fully utilized, tune the model parameters,

and re- run the job. Repeat the process until the shape resources are fully utilized.

Start with the strongest compute shape Job's support and monitor the Job Run metrics and

time required to complete the model training. Tune the model so that it utilizes as much

compute resources as possible, even at an increased cost.

Start with a smaller shape and monitor the utilization metrics and time required to complete

the model training. If the compute shape is fully utilized, change to compute that has more

resources and re-run the job. Repeat the process until the processing time does not

improve.

Configure the deployment infrastructure.

Execute the inference logic code.

Define the compute scaling strategy.

Define the inference server dependencies