Give Push to your Success with Oracle Cloud Infrastructure 1z0-1110-22 Exam Questions [Q34-Q56]

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Give Push to your Success with Oracle Cloud Infrastructure 1z0-1110-22 Exam Questions

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Oracle 1z0-1110-22 Certification Exam is designed for professionals who want to demonstrate their expertise in using Oracle Cloud Infrastructure for data science. Oracle Cloud Infrastructure Data Science 2022 Professional certification exam is the latest addition to the Oracle Cloud Infrastructure Data Science portfolio, and it is designed to test the candidates’ knowledge and skills in using the Oracle Cloud Infrastructure Data Science platform for building, deploying, and managing machine learning models.

NO.34 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.

NO.35 You are building a model and need input that represents data as morning, afternoon, or evening. However, the data contains a time stamp. What part of the Data Science life cycle would you be in when creating the new variable?

Model type selection

Model validation

Data access

Feature engineering

NO.36 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

NO.37 The Oracle AutoML pipeline automates hyperparameter tuning by training the model with different parameters in parallel. You have created an instance of Oracle AutoML as ora-cle_automl and now you want an output with all the different trials performed by Oracle Au-toML. Which of the following command gives you the results of all the trials?

Oracle.automl.visualize_algorith_selection_trails()

Oracle.automl.visualize_adaptive_sampling_trails()

Oracle.automl.print_trials()

Oracle.automl.visualize_tuning_trails()

NO.38 You have created a conda environment in your notebook session. This is the first time you are working with published conda environments. You have also created an Object Storage bucket with permission to manage the bucket Which TWO commands are required to publish the conda environment?

odsc conda publish –slug <SLUG>

odsc conda create –file manifest.yaml

odsc conda init -b <your-bucket-name> -a <api_key or resource_principal>

odsc conda list –override

NO.39 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

NO.40 You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?

The path to the arhive.zip file.

The local path to your pySpark script.

The compartment of the Data Flow application.

The bucket used to read/write the pySpark script in Object Storage.

The display name of the application.

NO.41 You are a data scientist with a set of text and image files that need annotation, and you want to use Oracle Cloud Infrastructure (OCI) Data Labeling. Which of the following THREE an-notation classes are supported by the tool.?

Object Detection

Named Entity Extraction

Classification (single/multi label)

Key-Point and Landmark

Polygonal Segmentation

Semantic Segmentation

NO.42 After you have created and opened a notebook session, you want to use the Accelerated Data Science (ADS) SDK to access your data and get started with exploratory data analysis. From which TWO places can you access the ADS SDK?

Oracle Autonomous Data Warehouse

Oracle Machine Learning

Conda environments in Oracle Cloud infrastructure (OCI) Data Science.

Python Package Index (PyPi)

Oracle Big Data Service

NO.43 The feature type TechJob has the following registered validators: Tech-Job.validator.register(name=’is_tech_job’, handler=is_tech_job_default_handler) Tech-Job.validator.register(name=’is_tech_job’, handler= is_tech_job_open_handler, condi-tion=(‘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 sales 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.

NO.44 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.

NO.45 As a data scientist, you are working on a global health data set that has data from more than 50 countries. You want to encode three features, such as ‘countries’, ‘race’, and ‘body organ’ as categories. Which option would you use to encode the categorical feature?

DataFramLabelEncode()

auto_transform()

OneHotEncoder()

show_in_notebook()

NO.46 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.

NO.47 You have a data set with fewer than 1000 observations, and you are using Oracle AutoML to build a classifier. While visualizing the results of each stage of the Oracle AutoML pipeline, you notice that no visualization has been generated for one of the stages. Which stage is not visualized?

Feature selection

Algorithm selection

Adaptive sampling

Hyperparameter tuning

NO.48 You realize that your model deployment is about to reach its utilization limit. What would you do to avoid the issue before requests start to fail?

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.

NO.49 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

NO.50 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.

NO.51 You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS supported data formats?

DOCX

Pandas DataFram

JSON

Raw Images

XML

NO.52 You have an embarrassingly parallel or distributed batch job on a large amount of data running using Data Science Jobs What would be the best approach to run the workload?

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

Reconfigure the job run because Data science jobs does not support embarrassingly parallel.

Create a new job for every job run that you have to run in parallel, because the Date Science Jobs service can have only one job run per job.

NO.53 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.

NO.54 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.

NO.55 You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your condo environment. After running this command, you get a network timeout error. What might be missing from your networking configuration?

Service Gateway with private subnet access.

NAT Gateway with public internet access.

FastConnect to an on-premises network.

Primary Virtual Network Interface Card (VNIC).

Oracle 1z0-1110-22 exam is a challenging but rewarding certification that can help data science professionals demonstrate their expertise in cloud-based data science solutions. By passing 1z0-1110-22 exam, candidates can enhance their career prospects, gain recognition for their skills and knowledge, and contribute to the growing field of data science in the cloud.

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