Get 2024 Free Oracle 1z0-1110-23 Exam Practice Materials Collection [Q47-Q65]

1z0-1110-23 Oracle

admin • January 19, 2024

Get 2024 Free Oracle 1z0-1110-23 Exam Practice Materials Collection [Q47-Q65]

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Get 2024 Free Oracle 1z0-1110-23 Exam Practice Materials Collection

Get Latest and 100% Accurate 1z0-1110-23 Exam Questions

Oracle 1z0-1110-23 Exam Syllabus Topics:

Topic	Details
Topic 1	Create and Manage Spark Applications using Data Flow and OCI Data Science Design and Set up OCI Data Science Workspace
Topic 2	Configure and manage source code in Code Repositories (Git) Understand the capabilities of Accelerated Data Science(ADS) SDK
Topic 3	Monitor & Log using MLOps Practices Access data from different sources
Topic 4	Obtain Global & Local Model Explanations Create and Export a Dataset using OCI Data Labeling
Topic 5	reate and Use automated ML capability from Oracle AutoML Use Pipelines to automate machine learning workflow
Topic 6	Create & Manage Jobs for custom tasks Discuss OCI Data Science Overview & Concepts

NEW QUESTION 47
You want to write a Python script to create a collection of different projects for your data science team. Which Oracle Cloud Infrastructure (OCI) Data Science interface would you use?

The OCI Software Development Kit (SDK)

OCI Console

Command line interface (CLI)

Mobile App

NEW QUESTION 48
While reviewing your data, you discover that your data set has a class imbalance. You are aware that the Accelerated Data Science (ADS) SDK provides multiple built-in automatic transformation tools for data set transformation. Which would be the right tool to correct any imbalance between the classes?

visualize_transforms ()

auto_transform()

sample ()

suggest_recommendations()

NEW QUESTION 49
You want to build a multistep machine learning workflow by using the Oracle Cloud Infrastructure (OCI) Data Science Pipeline feature. How would you configure the conda environment to run a pipeline step?

Configure a compute shape.

Configure a block volume.

Use command-line variables.

Use environmental variables

NEW QUESTION 50
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.

NEW QUESTION 51
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

NEW QUESTION 52
You want to make your model more frugal to reduce the cost of collecting and processing data.
You plan to do this by removing features that are highly correlated. You would like to create a heat map that displays the correlation so that you can identify candidate features to remove.
Which Accelerated Data Science (ADS) SDK method is appropriate to display the comparability between Continuous and Categorical features?

pearson_plot()

cramersv_plot()

correlation_ratio_plot()

corr()

NEW QUESTION 53
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{}

NEW QUESTION 54
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

NEW QUESTION 55
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

NEW QUESTION 56
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.

NEW QUESTION 57
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

NEW QUESTION 58
You have trained three different models on your data set using Oracle AutoML. You want to visualize the behavior of each of the models, including the baseline model, on the test set. Which class should be used from the Accelerated Data Science (ADS) SDK to visually compare the models?

EvaluationMetrics

ADSEvaluator

ADSExplainer

ADSTuner

NEW QUESTION 59
data scientist, you use the Oracle Cloud Infrastructure (OCI) Language service to train custom models. Which types of custom models can be trained?

Image classification, Named Entity Recognition (NER)

Text classification, Named Entity Recognition (NER)

Sentiment Analysis, Named Entity Recognition (NER)

Object detection, Text classification

NEW QUESTION 60
You want to make your model more parsimonious to reduce the cost of collecting and processing data. You plan to do this by removing features that are highly correlated. You would like to create a heat map that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method would be appropriate to display the correlation between Continuous and Categorical features?

Corr{}

Correlation_ratio_plot{}

Pearson_plot{}

Cramersv_plot{}

NEW QUESTION 61
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.

NEW QUESTION 62
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

NEW QUESTION 63
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.

NEW QUESTION 64
As a data scientist, you have stored sensitive data in a database. You need to protect this data by using a master encryption algorithm, which uses symmetric keys. Which master encryption algorithm would you choose in the Oracle Cloud Infrastructure (OCI) Vault service?

Triple Data Encryption Standard Algorithm

Elliptical Curve Cryptography Digital Signature Algorithm

Advanced Encryption Standard Keys

Rivert-Shamir-Adleman Keys

NEW QUESTION 65
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?

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

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

Global and local behaviours of machine learning models are similar.

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

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