Pass Oracle 1z0-1110-22 Exam With Practice Test Questions Dumps Bundle [Q28-Q45]

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Pass Oracle 1z0-1110-22 Exam With Practice Test Questions Dumps Bundle

2023 Valid 1z0-1110-22 test answers & Oracle Exam PDF

Obtaining the Oracle 1z0-1110-22 certification is a great way for data scientists to advance their careers and increase their earning potential. Oracle Cloud Infrastructure Data Science 2022 Professional certification demonstrates to potential employers that a candidate has the knowledge and skills necessary to use Oracle Cloud Infrastructure for data science projects. In addition, Oracle is a highly respected company in the tech industry, and having a certification from Oracle can help data scientists stand out in a crowded job market. Overall, the Oracle 1z0-1110-22 certification exam is a valuable tool for data scientists who want to take their careers to the next level.

Q28. You have just received a new data set from a colleague. You want to quickly find out summary information about the data set, such as the types of features, total number of observations, and data distributions, Which Accelerated Data Science (ADS) SDK method from the AD&Dataset class would you use?

Show_in_notebook{}

To_xgb{}

Compute{}

Show_corr{}

Q29. Select two reasons why it is important to rotate encryption keys when using Oracle Cloud In-frastructure (OCI) Vault to store credentials or other secrets.?

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

Key rotation reduces risk if a key is ever compromised.

Key rotation improves encryption efficiency.

Periodically rotating keys make it easier to reuse key.

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

Q30. For your next data science project, you need access to public geospatial images. Which Oracle Cloud service provides free access to those images?

Oracle Big Data Service

Oracle Analytics Claud

Oracle Cloud Infrastructure (OCI) Data Science

Oracle Open Data

Q31. Six months ago, you created and deployed a model that predicts customer churn for a call center. Initially, it was yielding quality predictions. However, over the last two months, users have been questioning the credibility of the predictions. Which TWO methods customer churn would you employ to verify the accuracy of the model?

Redeploy the model

Retrain the model

Operational monitoring

Validate the model using recent data

Drift monitoring

Q32. You want to write a Python script to create a collection of different projects for your data sci-ence team. Which Oracle Cloud Infrastructure (OCI) Data Science Interface would you use?

Programming Language Software Development Kit (SDK)

Mobile App

Command Line Interface (CLI)

OCI Console

Q33. You are attempting to save a model from a notebook session to the model catalog by using the Accelerated Data Science (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 networking configuration allows access to the Oracle Cloud Infrastructure (OCI) services through a Service Gateway.

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

The dynamic group’s matching rule exists for notebook sessions in this compartment.

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

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

Q34. You have received machine learning model training code, without clear information about the optimal shape to run the training on. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?

Start with the strangest compute shape Jobs 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 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 rerun the job. Repeat the process until the shape resources are fully utilized.

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.

Q35. You are working as a data scientist for a healthcare company. They decide to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended steps to develop a PySpark application in Oracle Cloud Infrastructure (OCI) Data Science?

Launch a notebook session. Configure core-site.xml. Install a PySPark conda environ-ment.

Develop your PySpark application Create a Data Flow application with the Ac-celerated Data Science (ADS) SOK

Configure core-site.xml. Install a PySPark conda environment. Create a Data Flow application with the Accelerated Data Science (ADS) SDK Develop your PySpark ap-plication. Launch a notebook session.

Launch a notebook session. Install a PySpark conda environment. Configure coresite. xml.

Develop your PySpark application. Create a Data Flow application with the Ac-celerated 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) SOK. Develop your PySpark application

Q36. What preparation steps are required to access an Oracle AI service SDK from a Data Science notebook session?

Call the Accented Data Science (ADS) command to enable Al integration

Create and upload the API signing key and config file

Import the REST API

Create and upload execute.py and runtime.yaml

Q37. 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?

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

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

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

Global and local behaviors of machine learning models are similar.

Q38. Using Oracle AutoML, you are tuning hyperparameters on a supported model class and have specified a time budget. AutoML terminates computation once the time budget is exhausted. What would you expect AutoML to return in case the time budget is exhausted before hy-perparameter tuning is completed?

A hyperparameter configuration with a minimum learning rate is returned.

The current best-known hyperparameter configuration is returned.

A random hyperparameter configuration is returned.

The last generated hyperparameter configuration is returned.

Q39. During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most effi-cient way to do this with Data Science Jobs?

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

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

Q40. Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to data sets from reference libraries and index websites, such as scikit-learn?

ADSTurner

DatasetFactory

SecretKeeper

Dataset Browser

Q41. You are a data scientist designing an air traffic control model, and you choose to leverage Or-acle 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?

Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning.

Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning.

Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning.

Algorithm selection, Adaptive sampling. Feature selection, Hyperparameter tuning.

Q42. Where do calls to stdout and stderr from score.py go in a model deployment?

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 file that was defined for them on the Virtual stachine (VM).

The OCI console.

Q43. As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those pa-rameters with Oracle Data Science Jobs?

Create a new job every time you need to run your code and pass the parameters as en-vironment variables.

Create your code to expect different parameters as command line arguments, and create it new job every time you run the code.

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 a new no by setting the required parameters in your code, and create a new job for mery code change.

Q44. When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) Data Science model catalog, you create a score.py file. What is the purpose of the score.py fie?

Define the compute scaling strategy.

Configure the deployment infrastructure.

Define the inference server dependencies.

Execute the inference logic code

Q45. You have created a model, and you want to use the Accelerated Data Science (ADS) SDK to deploy this model. Where can you save the artifacts to deploy this model with ADS?

Model Depository

Model Catalog

OCI Vault

Data Science Artifactory

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