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Oracle 1z0-1110-22 Exam Syllabus Topics:
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NEW QUESTION 21
Which of the following TWO non-open source JupyterLab extensions has Oracle Cloud In-frastructure (OCI) Data Science developed and added to the notebook session experience?
- A. Command Palette
- B. Notebook Examples
- C. Table of Contents
- D. Environment Explorer
- E. Terminal
Answer: B,D
NEW QUESTION 22
What preparation steps are required to access an Oracle AI service SDK from a Data Science notebook session?
- A. Import the REST API
- B. Create and upload the API signing key and config file
- C. Call the Accented Data Science (ADS) command to enable Al integration
- D. Create and upload execute.py and runtime.yaml
Answer: B
NEW QUESTION 23
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. The dynamic group's matching rule exists for notebook sessions in this compartment.
- B. The model artifact is saved to the block volume of the notebook session.
- C. The policy for your user group grants manage permissions for the model catalog in this compartment.
- D. The policy for the dynamic group grants manages permissions for the model catalog in this compartment.
- E. The networking configuration allows access to the Oracle Cloud Infrastructure (OCI) services through a Service Gateway.
Answer: C,D
NEW QUESTION 24
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?
- A. 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.
- B. 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.
- C. 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.
- D. 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.
Answer: A
NEW QUESTION 25
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?
- A. Create your code to expect different parameters as command line arguments, and create it new job every time you run the code.
- B. Create a new no by setting the required parameters in your code, and create a new job for mery code change.
- C. 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.
- D. Create a new job every time you need to run your code and pass the parameters as en-vironment variables.
Answer: C
NEW QUESTION 26
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?
- A. Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning.
- B. Algorithm selection, Adaptive sampling. Feature selection, Hyperparameter tuning.
- C. Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning.
- D. Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning.
Answer: B
NEW QUESTION 27
You trained a model to predict housing prices for your city. Which two metrics from the Ac-celerated Data Science (ADS) Evaluation class can be used to evaluate the regression model you just trained?
- A. F-1 Score
- B. Weighted Recall
- C. Explained Variance Score
- D. Weighted Precision
- E. Mean Absolute Error
Answer: C,E
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. 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.
- B. 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 com-pute shape, and capy your flies and data from your temporary bucket onto your new notebook session.
- C. Deactivate your notebook session, provision a new notebook session on larger compute shape, and re-create all your file changes.
- D. 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.
Answer: A
NEW QUESTION 29
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?
- A. 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.
- B. 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.
- C. Create the job in Data Science Jobs and then start the number of simultaneous job runs required for your workload.
- D. Reconfigure the job run because Data science jobs does not support embarrassingly parallel.
Answer: C
NEW QUESTION 30
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?
- A. odsc conda init -b <your-bucket-name> -a <api_key or resource_principal>
- B. odsc conda list --override
- C. odsc conda publish --slug <SLUG>
- D. odsc conda create --file manifest.yaml
Answer: A,C
NEW QUESTION 31
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. Execute the is_tech_job_open_handler handler.
- C. Throw an error because the system cannot determine which handler to run.
- D. Execute the is_tech_job_closed_handler handler.
Answer: C
NEW QUESTION 32
The Accelerated Data Science (ADS) model evaluation classes support different types of ma-chine learning modeling techniques Which THREE types of modeling techniques are supported by ADS Evaluators?
- A. Multiclass Classification
- B. K-Means Clustering
- C. Recurrent Neural Network
- D. Regression Analysis
- E. Principal Component Analysis
- F. Binary Classification
Answer: A,D,F
NEW QUESTION 33
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 inference server dependencies.
- C. Configure the deployment infrastructure.
- D. Define the compute scaling strategy.
Answer: C
NEW QUESTION 34
You are a data scientist working for a manufacturing company, you have developed a fore-casting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact.?
- A. Score.py
- B. Requirement.txt
- C. Runtime.yaml
- D. Model_artifact_validate.py
Answer: B
NEW QUESTION 35
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 Language
- C. OCI Anomaly Detection
- D. OCI Translation
Answer: A
NEW QUESTION 36
You want to ensure that all stdout and stderr from your code are automatically collected and logged, without implementing additional logging in your code. How would you achieve this with Data Science Jobs?
- A. On job creation, enable logging and select a log group. Then, select either log or the op-tion to enable automatic log creation.
- B. Make sure that your code is using the standard logging library and then store all the logs to Check Storage at the end of the job.
- C. You can implement custom logging in your code by using the Data Science Jobs logging.
- D. Data Science Jots does not support automatic fog collection and storing.
Answer: C
NEW QUESTION 37
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image dat a. Which of the following THREE are possible ways to annotate an image in Data Labeling?
- A. Adding multiple labels to an image.
- B. Adding labels to an image using object detection, by drawing bounding boxes to an im-age.
- C. Adding labels to an image by drawing bounding box to an image, is not supported by Data Labeling
- D. Adding labels to image using semantic segmentation, by drawing multiple bounding boxes to an image.
- E. Adding a single label to an image.
Answer: A,B,E
NEW QUESTION 38
You are using a third-party Continuous Integration/Continuous Delivery (CI/CD) tool to create a pipeline for preparing and training models. How would you integrate a third-party tool outside Oracle Cloud Infrastructure (OCI) to access Data Science Jobs?
- A. Data Science Jobs Data Science Jobs is not accessible from outside OCI.
- B. Third-party software can access Data Science Jobs by using any of the OCI Software Development Kits (SDKs).
- C. Data Science Jobs does not accept code from third-party tools, therefore you need to run the pipeline externally.
- D. Third-party tools use authentication keys to create and run.
Answer: B
NEW QUESTION 39
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?
- A. DatasetFactory
- B. SecretKeeper
- C. ADSTurner
- D. Dataset Browser
Answer: D
NEW QUESTION 40
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?
- A. Oracle Machine Learning
- B. Conda environments in Oracle Cloud infrastructure (OCI) Data Science.
- C. Oracle Big Data Service
- D. Python Package Index (PyPi)
- E. Oracle Autonomous Data Warehouse
Answer: A,B
NEW QUESTION 41
You are using Oracle Cloud Infrastructure Anomaly Detection to train a model to detect anomalies in pump sensor dat a. How does the required False Alarm Probability settings affect an anomaly detection model?
- A. It Adds a score to each signal indicating the probability that it is false alarm.
- B. It changes the sensitivity of the model to detect anomalies.
- C. It determines how many false alarms occur before an error message is generated.
- D. It is used to disable the reporting of false alarm.
Answer: B
NEW QUESTION 42
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 behavior of a machine learning model may be complex, while the local behavior may be approximated with a simpler surrogate model.
- B. Local explanation techniques are model agnostic, while global explanation techniques are not.
- C. Global and local behaviors of machine learning models are similar.
- D. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models.
Answer: A
NEW QUESTION 43
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