Latest 1z0-1110-23 Exam Dumps Oracle Exam from Training Expert Real4Prep
Pass Oracle Oracle Cloud Infrastructure Data Science 2023 Professional PDF Dumps | Recently Updated 80 Questions
NEW QUESTION # 16
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?
- A. Programming Language Software Development Kit (SDK)
- B. OCI Console
- C. Command Line Interface (CLI)
- D. Mobile App
Answer: A
NEW QUESTION # 17
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?
- A. Execute the inference logic code
- B. Define the compute scaling strategy.
- C. Configure the deployment infrastructure.
- D. Define the inference server dependencies.
Answer: C
NEW QUESTION # 18
3. 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 file?
- A. Define the inference server dependencies
- B. Configure the deployment infrastructure.
- C. Execute the inference logic code.
- D. Define the compute scaling strategy.
Answer: C
NEW QUESTION # 19
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?
- A. The OCI Software Development Kit (SDK)
- B. OCI Console
- C. Command line interface (CLI)
- D. Mobile App
Answer: A
NEW QUESTION # 20
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?
- A. Punctuation correction
- B. Table extraction
- C. Topic classification
- D. Sentiment analysis
- E. Sentence diagramming
Answer: C,D
NEW 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?
- A. A random hyperparameter configuration is returned.
- B. The current best-known hyperparameter configuration is returned.
- C. The last generated hyperparameter configuration is returned
- D. A hyperparameter configuration with a minimum learning rate is returned.
Answer: B
NEW QUESTION # 22
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?
- A. Pearson_plot{}
- B. Cramersv_plot{}
- C. Correlation_ratio_plot{}
- D. Corr{}
Answer: C
NEW QUESTION # 23
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?
- A. show_corr()
- B. show_in_notebook ()
- C. compute ()
- D. to_xgb ()
Answer: B
NEW QUESTION # 24
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?
- A. Your code using too much disk space. Refactor the code to identify the problem.
- B. On the job run, set the environment variable that helps increase the size of the storage.
- C. Edit the job, change the size of the storage of your job, and start a new job run.
- D. Create a new job with increased storage size and then run the job.
Answer: C
NEW QUESTION # 25
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?
- A. Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning Want any exam dump in pdf email me at [email protected] (Little Paid)
- B. Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning
- C. Algorithm selection, Adaptive sampling, Feature selection, Hyperparameter tuning
- D. Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning
Answer: B
NEW QUESTION # 26
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?
- A. To_xgb{}
- B. Show_corr{}
- C. Compute{}
- D. Show_in_notebook{}
Answer: D
NEW QUESTION # 27
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?
- A. sample()
- B. auto_transform()
- C. suggeste_recoomendations()
- D. visualize_transforms()
Answer: B
NEW QUESTION # 28
As you are working in your notebook session, you find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?
- A. Create a temporary bucket in Object Storage, write all your files and data to Object Storage, delete tur ctebook session, provision a new notebook session on a larger compute shape, and capy your flies and data from your temporary bucket onto your new notebook session.
- B. Ensure your files and environments are written to the block volume storage under the /home/datascience directory, deactivate the notebook session, and activate the notebook larger compute shape selected.
- C. Down your files and data to your local machine, delete your notebook session, provision tebook session on a larger compute shape, and upload your files from your local the new notebook session.
- D. Deactivate your notebook session, provision a new notebook session on larger compute shape, and re-create all your file changes.
Answer: B
NEW QUESTION # 29
As a data scientist, you create models for cancer prediction based on mammographic images.
The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix.
Model 1 has Test accuracy is 80% and recall is 70%.
* Model 2 has Test accuracy is 75% and recall is 85%.
Which model would you prefer and why?
- A. Model 1, because recall has lesser impact on predictions in this use case
- B. Model 2, because recall is high.
- C. Model 1, because the test accuracy is high.
- D. Model 2, because recall has more impact on predictions in this use se.
Answer: D
NEW QUESTION # 30
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?
- A. Redeploy the model
- B. Validate the model using recent data
- C. Drift monitoring
- D. Retrain the model
- E. Operational monitoring
Answer: B,C
NEW QUESTION # 31
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?
- A. Model validation
- B. Data access
- C. Feature engineering
- D. Model type selection
Answer: A
NEW QUESTION # 32
Where do calls to stdout and stderr from score.py go in a model deployment?
- A. The OCI console.
- B. The OCI Cloud Shell, which can be accessed from the console.
- C. The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment.
- D. The file that was defined for them on the Virtual stachine (VM).
Answer: C
NEW QUESTION # 33
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?
- A. Execute the is_tech_job_default_handler sales handler.
- B. Throw an error because the system cannot determine which handler to run.
- C. Execute the is_tech_job_open_handler handler.
- D. Execute the is_tech_job_closed_handler handler.
Answer: B
NEW QUESTION # 34
You are asked to prepare data for a custom-built model that requires transcribing Spanish video recordings into a readable text format with profane words identified.
Which Oracle Cloud service would you use?
- A. OCI Speech
- B. OCI Anomaly Detection
- C. OCI Language
- D. OCI Translation
Answer: A
NEW QUESTION # 35
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?
- A. Global behaviour of a machine learning model may be complex, while the local behaviour may be approximated with a simpler surrogate model.
- B. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models.
- C. Global and local behaviours of machine learning models are similar.
- D. Local explanation techniques are model-agnostic, while global explanation techniques are not
Answer: A
NEW QUESTION # 36
You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage bucket, train a model, and save it to the model catalog.
How would you design the authentication mechanism for the job?
- A. Store your personal OCI config file and keys in the Vault and access the Vault through the job run resource principal.
- B. Package your personal OCI config file and keys in the job artifact
- C. Create a pre-authenticated request (PAR) for the Object Storage bucket, and use that in the job code.
- D. 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.
Answer: D
NEW QUESTION # 37
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?
- A. A dynamic group has rules that matching the notebook sessions in it compartment.
- B. The policy for your user group grants manages permissions for the model catalog in this compartment.
- C. The model artifact is saved to the block volume of the notebook session.
- D. The policy for a dynamic group grant manages permissions for the model catalog in it compartment.
- E. The networking configuration allows access to Oracle Cloud Infrastructure services through a Service Gateway.
Answer: A,B
NEW QUESTION # 38
You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?
- A. The bucket used to read/write the pySpark script in Object Storage.
- B. The path to the arhive.zip file.
- C. The local path to your pySpark script.
- D. The compartment of the Data Flow application.
- E. The display name of the application.
Answer: A,C,E
NEW QUESTION # 39
You have created a Data Science project in a compartment called Development and shared it with a group of collaborators. You now need to move the project to a different compartment called Production after completing the current development iteration.
Which statement is correct?
- A. You cannot move a project to a different compartment after it has been created.
- B. Moving a project to a different compartment also moves its associated notebook sessions and models to the new compartment.
- C. Moving a project to a different compartment requires deleting all its associated notebook sessions and models first.
- D. You can move a project to a different compartment without affecting its associated notebook sessions and models
Answer: B
NEW QUESTION # 40
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?
- A. correlation_ratio_plot()
- B. corr()
- C. cramersv_plot()
- D. pearson_plot()
Answer: A
NEW QUESTION # 41
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