1z0-1110-23 Exam Dumps - Oracle Cloud Infrastructure Data Science 2023 Professional

Question # 4

You are a data scientist designing an air traffic control model, and you choose to leverage Oracle

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?

Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning

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Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning

Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning

Algorithm selection, Adaptive sampling, Feature selection, Hyperparameter tuning

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Question # 5

Which two statements are true about published conda environments?

They are curated by Oracle Cloud Infrastructure (OCI) Data Science.

The odac conda init command is used to configure the location of published conda

environments.

Your notebook session acts as the source to share published conda environments with team

members.

You can only create a published conda environment by modifying a Data Science conda

environment.

In addition to service job run environment variables, conda environment variables can be

used in Data Science Jobs.

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Question # 6

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 ()

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Question # 7

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?

odac conda publish --slug

odsc conda list --override

odsc conda init --bucket_namespace --bucket_name

odsc conda create --file manifest.yaml

conda activate /home/datascience/conda/

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Question # 8

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 efficient way to

do this with Data Science Jobs?

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.

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Question # 9

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()

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Question # 10

You have received machine learning model training code, without clear information about the

optimal shape to run the training. How would you proceed to identify the optimal compute shape

for your model training that provides a balanced cost and processing time?

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.

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Question # 11

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?

DataLabeling

DatasetBrowser

SecretKeeper

ADSTuner

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Question # 12

You are a data scientist leveraging the Oracle Cloud Infrastructure (OCI) Language AI service for various types of text analyses. Which TWO capabilities can you utilize with this tool?

Table extraction

Punctuation correction

Sentence diagramming

Topic classification

Sentiment analysis

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Question # 13

You want to use ADSTuner to tune the hyperparameters of a supported model you recently

trained. You have just started your search and want to reduce the computational cost as well as

access the quality of the model class that you are using.

What is the most appropriate search space strategy to choose?

Detailed

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

Perfunctory

Pass a dictionary that defines a search space

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Question # 14

Six months ago, you created and deployed a model that predicts customer churn for a call

centre. Initially, it was yielding quality predictions. However, over the last two months, users are

questioning the credibility of the predictions.

Which two methods would you employ to verify the accuracy of the model?

Retrain the model

Validate the model using recent data

Drift monitoring

Redeploy the model

Operational monitoring

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Question # 15

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

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Question # 16

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.

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Question # 17

Which feature of the Oracle Cloud Infrastructure (OCI) Vision service helps you generate in-dexing tags for a collection of marketing photographs?

Document classification

Image classification

Text recognition

Key Value extraction

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Question # 18

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, the total number of observations, and

distributions of the data. Which Accelerated Data Science (ADS) SDK method from the ADSDataset

class would you use?

show_corr()

to_xgb ()

compute ()

show_in_notebook ()

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Question # 19

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

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Question # 20

You have an embarrassingly parallel or distributed batch job on a large amount of data that you

consider running using Data Science Jobs. What would be the best approach to run the 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.

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.

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Question # 21

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 hyperparameter

tuning is completed?

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

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Question # 22

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

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Question # 23

You train a model to predict housing prices for your city. Which two metrics from the

Accelerated Data Science (ADS) ADSEvaluator class can you use to evaluate the regression model?

Explained Variance Score

F-1 Score

Weighted Precision

Weighted Recall