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Which statement about dynamic groups is true?. They define what Data Science principals, such as users and resources, have access to in OCI. They are individual users that are grouped in OCI by administrators and granted access to Data Science resources within compartments. They have matching rules, whereis replaced by the identifier of the compartment created for Data Science. They are a logical grouping of resources that can be accessed only by certain groups that have received administrator permission.

Which statement about resource principals is true?. A resource principal is a feature of IAM that enables resources to be authorized principal actors. When you authenticate using a resource principal, you need to create and manage credentials to access OCI resources. A resource principal is not a secure way to authenticate to resources, compared to the OCI configuration and API key approach. The Data Science service does not provide authentication via a notebook session's or job run's resource principal to access other OCI resources.

Which allows the sharing and loading back of ML models into a Notebook session?. Model taxonomy. Model provenance. Model deployment. Model catalog.

Which OCI Data Science interaction method can function without the need of scripting?. Language SDKs. REST API. CLI. OCI Console.

What does the Data Science Service template in Oracle Resource Manager (ORM) NOT automatically create?. Dynamic groups. Individual Data Science users. Policies for a basic use case. Required user groups.

Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?. Projects. Model catalog. Notebook session. Jobs.

What is feature engineering in machine learning used for?. To help understand the data set features. To transform existing features into new ones. To interpret ML models. To perform parameter tuning.

What happens when a notebook session is deactivated?. The underlying compute instance stops. The block volume attached to the Notebook session is permanently deleted. Compute cost increases due to frequent deactivation. The data on the boot volume is not preserved.

Which is a unique feature of the published Conda environment?. It provides a comprehensive environment to solve business use cases. It allows you to save the Conda environment to an Object Storage bucket. It provides availability on Notebook session reactivation. It allows you to save the Conda environment in a block volume.

Which model has an open source, open model format that allows you to run machine learning models on different platforms?. PySpark. TensorFlow. ONNX. PyTorch.

What is a conda environment?. An environment deployment system on Oracle AI. A collection of kernels. A system that manages package independencies. An open source package and environment management system.

Which activity of managing a conda environment requires the conda environment to be activated in your terminal?. Cloning a Conda environment. Installing a Conda environment. Publishing a Conda environment. Modifying a Conda environment.

What is an accurate description of Git?. Git is a centralized version control system that allows data scientists and developers to track copious amounts of data. Git is a distributed version control system that allows you to track changes made to a set of files. Git is a centralized version control system that allows you to revert to previous versions of files as needed. Git is a distributed version control system that protects teams from simultaneous repo contributions and merge requests.

Which CLI command allows a customized Conda environment to be shared with co-workers?. odsc conda install. odsc conda publish. odsc conda modify. odsc conda clone.

Where are OCI secrets stored?. Autonomous Data Warehouse. OCI Vault. Oracle Databases. OCI Object Storage.

Which step is a part of AutoML pipeline?. Model Saved to Model Catalog. Feature Extraction. Feature Selection. Model Deployment.

You are working for a bank. You are required to analyze customer accounts' access data and flag any irregular access attempts. Which OCI Data Science operator are you most likely to use?. Forecasting. Anomaly. PII.

Which function represents the difference between the predictive value and the target value?. Optimizer function. Cost function. Update function. Fit function.

Which stage in the machine learning life cycle helps identify imbalances present in data?. Data Modeling. Data Exploration. Data Access. Data Monitoring.

Which activity is NOT a part of the machine learning life cycle?. Modeling. Database Management. Data Access. Model Deployment.

What do you use the score.py file for?. Defining the scaling strategy. Executing the inference logic code. Configuring the deployment infrastructure. Defining the required Conda environments.

As a data scientist, you require a pipeline to train ML models. When can a pipeline run be initiated?. During the pipeline run state. After it is created. After the Active state. Before the Active state.

Which statement is true about machine learning models?. Static predictions become increasingly accurate over time. A high-quality model will not need to be retrained as new information is received. Data models are more static and generally require fewer updates than software code. Model performance degrades over time due to changes in data.

You want to change the autoscaling configuration for infrastructure and non-infrastructure fields for an existing model deployment in an Active state in Oracle Data Science. Which statement is true?. Infrastructure-related aspects can't be modified for a model deployment, regardless of state. Non-infrastructure-related aspects can't be modified for an active model deployment. You can modify the Autoscaling Scaling Policy fields and other configurations simultaneously regardless of its state. You must disable the model deployment to update the Autoscaling Scaling Policy fields and other configurations.

Which statement is true about logs for Oracle Cloud Infrastructure Jobs?. Integrating Data Science Jobs resources with Logging is mandatory. Logs are automatically deleted when the job and job run are deleted. All stdout and stderr are automatically stored when automatic log creation is enabled. Each job run sends outputs to a single log for that job.

In the OCI Console, as part of Monitoring, of what is triggering a Pager Duty notification an example?. Action. Event. Rule. Function.

Why would you use a mini batch when processing a job in Data Science Jobs?. You want several distributed models to run simultaneously. You want to process data frequently. You do not need to process data quickly. There is a small amount of total data to process.

Which statement is true about Oracle Cloud Infrastructure Data Science Jobs?. You must create and manage your own Jobs infrastructure. You must use a single Shell/Bash or Python artifact to run a job. Jobs provides the infrastructure to run a process on demand. Jobs comes with a set of standard tasks that cannot be customized.

Which step is unique to MLOps, as opposed to DevOps?. Continuous Delivery. Continuous Training. Continuous Integration. Continuous Deployment.

Which OCI service provides a scalable environment for developers and data scientists to run Apache Spark applications at scale?. Data Science. Anomaly Detection. Data Labeling. Data Flow.

You are a researcher who requires access to large data sets. Which OCI service would you use?. ADW. Oracle Open Data. OCI Data Science. Oracle Databases.

Which option indicates the three types of data used for Data Labeling?. Graphic, text, document. Image, text, document. Text, audio, video. Image, audio, document.

What is the name of the machine learning library used in Apache Spark?. GraphX. Structured Streaming. HadoopML. MLlib.

Which Oracle Cloud Infrastructure (OCI) Data Science policy is invalid?. Allow group DataScienceGroup to use virtual-network-family in compartment DataScience. Allow group DataScienceGroup to use data-science-model-sessions in compartment DataScience. Allow dynamic-group DataScienceDynamicGroup to manage data-science-projects in compartment DataScience. Allow dynamic-group DataScienceDynamicGroup to manage data-science-family in compartment DataScience.

Which is NOT a valid OCI Data Science notebook session approach?. Ensure you don't execute long-running Python processes in a notebook cell. Run the process directly in the terminal and use Python logging to get updates on the progress of your job. Avoid having multiple users in the same notebook session due to the possibility of resource contention and write conflicts. While connecting to data in OCI Object Storage from your notebook session, the best practice is to make a local copy on the device and then upload it to your notebook session block volume. Authenticate using your notebook session's resource principal to access other OCI resources. Resource principals provide a more secure way to authenticate to resources compared to the OCI configuration and API key approach.

You are working as a data scientist for a healthcare company. You have analyzed a series of neurophysiological data on OCI Data Science and have developed a convolutional neural network (CNN) classification model. It predicts the source of seizures in drug-resistant epileptic patients. You created a model artifact with all the necessary files. When you deployed the model, it failed to run because you did not point to the correct conda environment in the model artifact. Where would you provide instructions to use the correct conda environment?. score.py. runtime.yaml. requirements.txt. model_artifact_validate.py.

You have an image classification model in the model catalog which is deployed as an HTTP endpoint using model deployments. Your tenancy administrator is seeing increased demands and has asked you to increase the load balancing bandwidth from the default of 10 Mbps. You are provided with the following information: -Payload size in KB: 1024 -Estimated requests per second: 120 requests/second -Buffer percentage: 20% What is the optimal load balancing bandwidth to redeploy your model?. 452 Mbps. 52 Mbps. 7052 Mbps. 1152 Mbps.

You want to create an anomaly detection model using the OCI Anomaly Detection service that avoids as many false alarms as possible. False Alarm Probability (FAP) indicates model performance. How would you set the value of False Alarm Probability?. High. Low. Zero. Use a function.

A team wants to use CPU utilization as a metric to trigger autoscaling. Which type of autoscaling policy should they configure?. Manual scaling. Custom scaling metric. Predefined metric. Load balancer scaling.

You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which two prevailing metrics would you use for evaluating the multiclass classification model?. Recall. R-squared. Explained variance score. F1 score. Mean squared error.

A company is running a job in OCI Data Science Jobs and wants to ensure that the infrastructure is deprovisioned immediately after the job completes to avoid unnecessary costs. What happens when the job ends?. The compute shape is reset to default. The job artifact is deleted. The infrastructure remains active for 30 days. The infrastructure is automatically deprovisioned.

A data scientist needs to securely access an external database from their notebook session. What is the best way to store the credentials?. Hardcode the credentials in the Jupyter Notebook. Share the credentials via email with team members. Save the credentials in OCI Vault and retrieve them programmatically when needed. Store the credentials in a plaintext configuration file.

Which resource types are included in the default matching rules of the Data Science Service template?. datasciencenetwork, datasciencedatabase, datasciencebackup. datascienceanalytics, datasciencemonitoring, datasciencebatchjob. datascienceobjectstorage, datasciencecomputeinstance, datasciencemodeltraining. datasciencemodeldeployment, datasciencenotebooksession, datasciencejobrun.

A data scientist is using an AI model to predict fraudulent transactions. A financial regulator asks why a specific transaction was flagged as fraud. Which technique should the data scientist use?. Feature Permutation Importance. What-If Explanation. Local Explanation. Global Explanation.

What is the primary advantage of using Conda environments in Data Science?. They help in compressing datasets for storage efficiency. They enable isolated software configurations for different projects. They provide faster GPU processing speeds. They replace the need for cloud storage in machine learning projects.

Which two statements are true about Oracle Cloud Infrastructure (OCI) Open Data Service?. Subscribers can pay and log into Open Data to view curated data sets that are otherwise not available to the public. Open Data is a dataset repository made for the people that create, use, and manipulate datasets. Open Data includes text and image data repositories for AI and ML. Audio and video formats are not available. Each dataset in Open Data consists of code and tooling usage examples for consumption and reproducibility. A primary goal of Open Data is for users to contribute to the data repositories in order to expand the content offered.

You have trained a binary classifier for a loan application and saved this model into the model catalog. A colleague wants to examine the model, and you need to share the model with your colleague. From the model catalog, which model artifacts can be shared?. Models and metrics only. Metadata, hyperparameters, and metrics only. Models, model metadata, hyperparameters, and metrics.

What is the primary goal of the loss function in model training?. To maximize the likelihood of data points fitting the model. To compare predicted values ​​with true target values ​​and quantify their difference. To determine the best algorithm for training the model. To update the model parameters to optimize performance.

You have custom data and you want to customize an off-the-shelf LLM and deploy it quickly. How can AI Quick Actions help you?. To pretrain the LLM. To deploy the off-the-shelf model. To fine-tune the model and deploy.

A company wants to integrate an LLM into its customer support chatbot using OCI. What is the fastest way to deploy and test the model?. Training a custom model from scratch using OCI AutoML. Using AI Quick Actions to quickly deploy a pretrained LLM. Manually configuring a model deployment using OCI SDK. Building a deep learning model in Jupyter Notebook.

What is the difference between a job and a job run in OCI Data Science Jobs?. A job is a single execution, while a job run is a template. A job is a template, while a job run is a single execution of that template. A job is used for model training, while a job run is used for batch inference. A job is immutable, while a job run can be modified.

What triggers the automation of the MLOps pipeline?. Manual intervention by data scientists. Changes in data, monitoring events, or calendar intervals. Random system updates. User feedback.

A data scientist is running a long-term experiment in an OCI notebook session. They need to save results even if they deactivate the session to reduce costs. What should they do?. Save results only in the boot volume, as it is retained indefinitely. Keep the session active indefinitely to prevent data loss. Use default networking to automatically back up results to OCI Object Storage. Store all results in the block storage, as it persists after deactivation.

What is the purpose of a dynamic group in OCI?. To group individual users for easier management. To manage API access for resources such as notebook sessions. To define storage limits for data science resources. To allocate computing resources dynamically.

A data scientist is working on a project to train a machine learning model to identify tigers in images. What is the first step they need to take before training the model?. Deploy the model. Label the images with "tiger" or "not tiger". Use OCI Vision Services. Analyze customer feedback.

A healthcare company needs to redact personal details (such as names, emails, and phone numbers) from patient records before sharing them with a research institute. Which operator is best suited for this task?. Forecasting Operator. Anomaly Detection Operator. PII Detection Operator. Clustering Operator.

While working with Git on Oracle Cloud Infrastructure (OCI) Data Science, you notice that two of the operations are taking more time than the others due to your slow internet speed. Which two operations would experience the delay?. Pushing changes to a remote repository. Moving changes into the staging area for the next commit. Making a commit that is taking a snapshot of the local repository for the next push. Updating the local repo to match the content from a remote repository. Converting an existing local project folder to Git repository.

How can a team ensure that data processing occurs before model training in a pipeline?. By increasing the block volume size. By setting dependencies between steps. By using the same programming language for all steps. By overriding the default configuration.

Which statement is true regarding autoscaling configuration for an existing model deployment in an Active state in Oracle Data Science?. You can modify the Autoscaling Scaling Policy fields and other configurations simultaneously. You must disable the model deployment to update the Autoscaling Scaling Policy fields. Changes to the Autoscaling Scaling Policy fields must occur one field at a time, without simultaneous changes to other configurations. Only non-infrastructure-related aspects can be modified for an active model deployment.

Arrange the following points in the correct Git Repository workflow order. 1. Install, configure, and authenticate Git. 2. Configure SSH keys for the Git repository. 3. Create a local and remote Git repository. 4. Commit files to the local Git repo. 5. Push the commit to the remote Git repo. 1, 2, 3, 4, 5. 2, 3, 1, 4, 5. 3, 5, 1, 2, 4. 4, 2, 3, 1, 5.

Once you deploy the LLM using AI Quick Actions, how can you invoke your model?. Through API. Through CLI. Through API and CLI. Through only CLI.

You have been given a collection of digital files required for a business audit. They consist of several different formats that you would like to annotate using Oracle Cloud Infrastructure (OCI) Data Labeling. Which three types of files could this tool annotate?. Images of computer server racks. A type-written document that details an annual budget. A collection of purchase orders for office supplies. Video footage of a conversation in a conference room. An audio recording of a phone conversation.

As a data scientist for a hardware company, you have been asked to predict the revenue demand for the upcoming quarter. You develop a time series forecasting model to analyze the data. Which is the correct sequence of steps to predict the revenue demand values ​​for the upcoming quarter?. Prepare model, verify, save, deploy, predict. Prepare model, deploy, verify, save, predict. Verify, prepare model, deploy, save. Predict, deploy, save, verify, prepare model.

What happens when a model deployment in OCI Data Science is deactivated?. The deployed model is permanently deleted, and predictions are no longer possible. The model deployment metadata is erased along with the model artifacts. The model's HTTP endpoint becomes unavailable, but metadata is preserved. The model remains active but stops accepting new inference requests.

A user wants to fetch data from an Autonomous Database in OCI without using a database wallet. What must they do?. Provide the hostname and port number in the connection_parameters dictionary. Use ads.read_sql without any additional parameters. Enable API authentication in the database console. Use an HTTP request to retrieve database records.

Which type of data is NOT available in Oracle Open Data?. Geospatial data from satellite systems. Protein sequences and genomic data. Financial transaction data. Annotated text files.

Which statement best describes Oracle Cloud Infrastructure Data Science Jobs?. Jobs allow you to define and run repeatable tasks on fully managed infrastructure. Jobs allow you to define and run repeatable tasks on customer-hosted infrastructure. Jobs allow you to define and run all Oracle Cloud DevOps workloads. Jobs allow you to define and run repeatable tasks on fully managed third-party cloud infrastructure.

You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which two prevailing metrics would you use for evaluating the multiclass classification model?. R-squared. Mean squared error. Recall. F1 score. Explained variance score.

A company has trained a machine learning model and wants to fine-tune it by experimenting with hyperparameter values ​​based on prior experience. What approach should they take?. Use the built-in perfunctory search strategy. Apply the detailed search space for broader tuning. Define a custom search space with specific hyperparameter values. Skip hyperparameter tuning altogether.

Which correlation method is used to measure the relationship between two categorical variables in ADS?. Pearson correlation coefficient. Spearman correlation coefficient. Cramer's V method. Chi-square test.

A data scientist wants to develop a PySpark application iteratively using a sample of their dataset. Which environment is recommended for this purpose?. OCI Compute. OCI Data Science notebook session. OCI Object Storage. OCI Virtual Cloud Network.

A data scientist updates an IAM policy to grant their notebook session access to an Object Storage bucket. However, the notebook still cannot access the bucket. What is the likely reason?. The IAM policy is incorrect. The resource principal token is still cached. The user needs to restart the entire OCI environment. Object Storage does not support access from notebooks.

A team wants to create a sophisticated autoscaling query that combines multiple metrics using logical operators. Which option should they use?. Predefined metrics. Custom scaling metric with NQL expressions. Cooldown periods. Load balancer scaling.

What is the primary reason for performing feature scaling in machine learning models?. To make the dataset smaller for faster computation. To bring features on to the same scale. To convert categorical data into numerical form. To automatically detect missing values ​​and fill them with mean or median.

A bike sharing platform has collected user commute data for the past three years. For increasing the profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which option has the correct order of the required machine learning tasks for building a model?. Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling. Data Access, Data Exploration, Feature Exploration, Feature Engineering, Modeling. Data Access, Feature Exploration, Data Exploration, Feature Engineering, Modeling. Data Access, Feature Exploration, Feature Engineering, Data Exploration, Modeling.

Which statement is incorrect regarding the benefits of autoscaling for model deployment in Oracle Data Science?. Autoscaling dynamically adjusts compute resources based on real-time demand, ensuring efficient handling of varying loads. By using autoscaling, the cost of deployment remains constant irrespective of resource utilization. Autoscaling, in conjunction with load balancers, enhances availability by rerouting traffic to healthy instances in case of instance failure. Users can set customizable triggers for autoscaling using MQL expressions to tailor the scaling behavior according to specific needs.

You are running a pipeline in OCI Data Science Service and want to override some of the pipeline's default settings. Which statement is true about overriding pipeline defaults?. Pipeline defaults cannot be overridden once the pipeline has been created. Pipeline defaults can be overridden only during pipeline creation. Pipeline defaults can be overridden before starting the pipeline execution. Pipeline defaults can be overridden only by an administrator.

What model parameter value are you most likely to use if you are not sure of your selection while configuring the Forecasting operator?. arima. prophet. auto. autots.

You are a data scientist working on census dataset. You have decided to use Oracle AutoML Pipeline for automating your machine learning task and want to ensure that two of the features ("Age" and "Education") are part of the final model that the AutoML creates. To ensure these features are not dropped during the feature selection phase, what would be the best way to define the min_features argument in your code?. 0 < min_features <= 2. min_features = ['Age', 'Education']. 0 < min_features <= 0.9. min_features = 'Age' && min_features = 'Education'.

Which is NOT a supported encryption algorithm in OCI Vault?. AES (Advanced Encryption Standard). RSA (Rivest-Shamir-Adleman). ECDSA (Elliptic Curve Digital Signature Algorithm). SHA-256 (Secure Hash Algorithm 256-bit).

What is the final step after running the Oracle Resource Manager stack for Data Science configuration?. Deleting the default compartment. Modifying the Terraform script in GitHub. Adding users to the automatically created user group. Creating an additional stack for security configuration.

You want to create a user group for a team of external data science consultants. The consultants should only have the ability to view data science resource details but not the ability to create, delete, or update data science resources. What verb should you write in the policy?. Read. Use. Inspect. Manage.

A team of data scientists is working on multiple machine learning models for fraud detection. They want to collaborate in a structured manner. What option is available to create a Data Science Project in OCI?. Can be created only through the OCI Console UI. Can be created only through the ADS SDK. Can be created through either the OCI Console UI or the ADS SDK. Can be created using a command-line interface (CLI) only.

You have just started as a data scientist at a healthcare company. You have been asked to analyze and improve a deep neural network model that was built based on the electrocardiogram records of patients. There are no details about the model framework that was built. What would be the best way to find more details about the machine learning models inside model catalog?. Refer to the code inside the model. Check for metadata tags. Check for Model Taxonomy details. Check for Provenance details.

A data scientist is working on a fraud detection model. They need to store the trained model so that it can be versioned, tracked, and later deployed without modification. Which feature should they use?. Model Deployment. Model Catalog. Model Explainability. Hyperparameter Tuning.

A company is running a job in OCI Data Science Jobs and wants to ensure that the infrastructure is deprovisioned immediately after the job completes to avoid unnecessary costs. What happens when the job ends?. The infrastructure remains active for 30 days. The infrastructure is automatically deprovisioned. The job artifact is deleted. The compute shape is reset to default.

What is the key difference between PDP (Partial Dependence Plot) and ICE (Individual Conditional Expectation) in ADS?. PDP provides feature-level insights, while ICE provides sample-level insights. PDP works only for categorical features, while ICE works only for continuous features. PDP is a supervised learning technique, while ICE is used for unsupervised learning. PDP is used for classification, while ICE is only used for regression.

Where are the training job outputs stored after fine-tuning is completed?. In the local storage of the training instance. In an OCI Object Storage bucket. Directly in the OCI Model Catalog. Directly in the OCI Model Catalog.

When deploying an RAG application to OCI Data Science, what is the correct sequence of steps you would need to follow? 1. Load documents. 2. Split documents. 3. Embed documents. 4. Create vector database from documents. 5. Create retriever. 1-2-3-4-5. 2-3-5-1-4. 3-1-2-5-4. 1-4-3-2-5.

Which two statements are true about Oracle Cloud Infrastructure (OCI) Open Data Service?. Subscribers can pay and log into Open Data to view curated data sets that are otherwise not available to the public. Open Data is a dataset repository made for the people that create, use, and manipulate datasets. Open Data includes text and image data repositories for AI and ML. Audio and video formats are not available. Each dataset in Open Data consists of code and tooling usage examples for consumption and reproducibility. A primary goal of Open Data is for users to contribute to the data repositories in order to expand the content offered.

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.

You need to build a machine learning workflow that has sequential and parallel steps. You have decided to use the Oracle Cloud Infrastructure (OCI) Data Science Pipeline feature. How is Directed Acyclic Graph (DAG) having sequential and parallel steps built using Pipeline?. Using Pipeline Designer. By running a Pipeline. Using dependencies. Using environmental variables.

You are using a git repository that is stored on GitHub to track your notebooks. You are working with another data scientist on the same project but in different notebook sessions. Which two statements are true?. To share your work, you commit it and push it to GitHub. Your coworker can then pull your changes on to their notebook session. It is a best practice that you and your coworker should work in the same branch because you are working on the same project. Once you have staged your changes, you run the git commit command to save a snapshot of the state of your code. Only one of you has to clone the GitHub repo as you can share it. You do not have to clone the GitHub repo as you can commit directly from the notebook session to GitHub.

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 parameters with Oracle Data Science Jobs?. Create a new job every time you need to run your code and pass the parameters as environment variables. Set up the code to accept different parameters as command-line arguments and create a new task each time the code runs. Set the required parameters in the code and create a new task each time the code changes. Set up the code to accept different parameters as environment variables or command-line arguments and set different values ​​each time the task runs.

You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many subfolders and classes. What is the best way to run this project as a Job?. Rewrite your code so that it is a single executable Python or Bash/Shell script file. ZIP the entire code project folder, upload it as a Job artifact on job creation, and set JOB_RUN_ENTRYPOINT to point to the main executable file. ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file. Zip the entire code project folder and upload it as a job artifact. The job will automatically identify the top-level file to run the code _main_.

You are setting up a fine-tuning job for a pre-trained model on Oracle Data Science. You obtain the pre-trained model from HuggingFace, define the training job using the ADS Python API, and specify the OCI bucket. The training script includes downloading the model and dataset. Which of the following steps will be handled automatically by the ADS during the job run?. Setting up the conda environment and installing additional dependencies. Specifying the replica and shape of instances required for the training job. Saving the outputs to OCI Object Storage once the training finishes. Fetching the source code from GitHub and checking out the specific commit.

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. 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.

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 add more instances. Reduce the load balancer bandwidth limit so that fewer requests come in. Update the deployment to use a larger virtual machine (more CPUs/memory). Delete the deployment. Update the deployment to use fewer instances.

Which approach does Oracle AutoML use to avoid the cold start problem?. Randomized hyperparameter tuning to generate diverse models. Exhaustive grid search to evaluate every possible model configuration. Genetic evolutionary algorithms to evolve new models dynamically. Meta-learning to predict algorithm performance on unseen data sets.

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?. ADSTuner doesn't need a search space to tune the hyperparameters. Perfunctory. Pass a dictionary that defines a search space. Detailed.

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 random hyperparameter configuration is returned. The last generated hyperparameter configuration is returned. The current best-known hyperparameter configuration is returned. A hyperparameter configuration with a minimum learning rate is returned.

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?. Package your personal OCI config file and keys in the job artifact. Store your personal OCI config file and keys in the Vault, and access the Vault through the job run resource principal. Create a pre-authentication request (PAR) for the object storage bucket and use it in the job code. Use the job run's resource principal as the signer in the job code, ensuring that a dynamic group is created for this job run and has appropriate access permissions to the object storage and model directories.

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?. You cannot move a project to a different compartment after it has been created. Moving a project to a different compartment requires deleting all its associated notebook sessions and models first. You can move a project to a different compartment without affecting its associated notebook sessions and models. Moving a project to a different compartment also moves its associated notebook sessions and models to the new compartment.

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 behavior of a machine learning model may be complex, while the local behavior may be approximated with a simpler surrogate model. Global and local behaviors of machine learning models are similar. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models. Local explanation techniques are model-agnostic, while global explanation techniques are not.

What detector in PII Operator are you likely to use if you need to obfuscate the detected sensitive information?. Anonymize. Mask. Remove.

You want to evaluate the relationship between feature values ​​and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?. Feature Dependence Explanations. Local Interpretable Model-Agnostic Explanations. Accumulated Local Effects. Feature Permutation Importance Explanations.

What is the sequence of steps you are likely to follow to use OCI Data Science Operator?. Install conda. Initialize operator. Configure operator. Run operator. Check results. Configure operator. Install conda. Initialize operator. Run operator. Check results. Check results. Install conda. Initialize operator. Run operator. Check results. Initialize operator. Install conda. Check results. Configure operator. Run operator.

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. odsc conda list --override. odsc conda create --file manifest.yaml. odsc conda init --bucket_namespace--bucket_name. conda activate /home/datascience/conda//.

A data scientist is working on a deep learning project with TensorFlow and wants to ensure the same environment can be shared with colleagues. What is the best approach?. Create a new Conda environment every time a colleague needs access. Store the Conda environment as a published Conda environment in Object Storage. Copy and paste the package list into a text file for manual installation. Manually install TensorFlow on each team member's machine.

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?. Make sure that your code is using the standard logging library and then store all the logs to Object Storage at the end of the job. You can implement custom logging in your code by using the Data Science Jobs logging service. Create your own log group and use a third-party logging service to capture job run details for log collection and storing. On job creation, enable logging and select a log group. Then, select either a log or the option to enable automatic log creation.

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?. Use command-line variables. Configure a block volume. Use environmental variables. Configure a compute shape.

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 policy for your user group grants manage permissions for the model catalog in this compartment. The networking configuration allows access to Oracle Cloud Infrastructure services through a Service Gateway. The dynamic group's policy grants model catalog management permissions in this partition. The dynamic group's permissions for matching rules and notebook sessions in this partition. Model artifacts, which are saved in the notebook session's block volume.

You want to install a list of Python packages on your data science notebook session while creating the instance. Which option will allow you to do the same?. Using runtime configuration. Using storage mounts. Invoking public endpoint.

Once the LangChain application is deployed to OCI Data Science, what are two ways to invoke it as an endpoint?. Use .predict method or Use CLI. Use CLI or Use .invoke(). Use .invoke() method or Use .predict method.

A data scientist is analyzing customer churn data and wants to visualize the relationship between monthly charges (a continuous variable) and churn status (a categorical variable). What is the best visualization that ADS will likely generate?. A violin plot. A scatterplot. A histogram. A line chart.

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 ADSDatasetclass would you use?. show_in_notebook(). compute(). to_xgb(). show_corr().

As a data scientist, you are working on a movie recommendation application where you have a very large movie dataset. Which Oracle Cloud Infrastructure (OCI) services should you use to develop interactive Spark applications and deploy Spark workloads?. Data Science and Vault. Data Integration and Vault. Analytics Cloud and Data Flow. Data Flow and Data Science.

You are working as a data scientist for a healthcare company. They decide to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended steps to develop a PySpark application in Oracle Cloud Infrastructure (OCI) Data Science?. Install the Spark conda environment. Configure core-site.xml. Start a notebook session. Create a dataflow application using the Accelerated Data Science (ADS) SDK. Develop a PySpark application. Start a notebook session. Install the PySpark conda environment. Configure core-site.xml. Develop a PySpark application. Start a notebook session. Configure core-site.xml. Install the PySpark conda environment. Develop a PySpark application. Create a dataflow application using the Accelerated Data Science (ADS) SDK. Configure core-site.xml. Install the PySpark conda environment. Create a dataflow application using the Accelerated Data Science (ADS) SDK. Develop a PySpark application. Start a notebook session.

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?. auto_transform(). fit_transform(). visualize_transforms(). get_transformed_dataset().

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 an exploratory data analysis. From which two places can you access or install the ADS SDK?. Conda environments in Oracle Cloud Infrastructure (OCI) Data Science. Oracle Autonomous Data Warehouse. Oracle Machine Learning (OML). Oracle Big Data Service. Python Package Index (PyPi).

You are a data scientist with a set of text and image files that need to be annotated, and you want to use the Oracle Cloud Infrastructure (OCI) Data Annotation Tool. Which three of the following annotation categories does the tool support?. Semantic Segmentation. Object Detection. Classification (Single/Multi-label). Named Entity Extraction. Keypoints and Labels. Polygon Segmentation.

You are a data scientist and have a large number of legal documents that needs to be classified. You decided to use OCI Data Labeling service to get your data labeled. What are the annotation classes available for annotating documents data using OCI Data Labeling service?. Single, Multiple, Key Value. Single, Multiple, Entity Extraction. Single, Multiple, Object Detection.

For your next data science project, you need access to public geospatial images. Which Oracle Cloud service provides free access to those images?. Oracle Big Data Service. Oracle Cloud Infrastructure Data Science. Oracle Analytics Cloud. Oracle Open Data.

You are a data scientist leveraging Oracle Cloud Infrastructure (OCI) Data Science to create a model and need some additional Python libraries for processing genome sequencing data. Which of the following THREE statements are correct with respect to installing additional Python libraries to process the data?. You can install any open-source package available on a publicly accessible Python Package Index (PyPI) repository. OCI Data Science allows root privileges in notebook sessions. You cannot install a library that's not preinstalled in the provided image. You can only install libraries using yum and pip as a normal user. You can install private or custom libraries from your own internal repositories.

You need to make sure that the model you have deployed using AI Quick Actions is responding with suitable responses. How can AI Quick Actions help here?. By fine-tuning the model. By evaluating the model. By deploying the model.

Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets. Key rotation allows you to encrypt no more than five keys at a time. Periodically rotating keys limits the amount of data encrypted by one key version. Key rotation reduces risk if a key is ever compromised. Periodically rotating keys make it easier to reuse keys. Key rotation improves encryption efficiency.

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?. Elliptical Curve Cryptography Digital Signature Algorithm. Triple Data Encryption Standard Algorithm. Rivest-Shamir-Adleman Keys. Advanced Encryption Standard Keys.

You are a data scientist working for a manufacturing company. You have developed a forecasting 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. What file should be modified to include the missing libraries?. requirements.txt. score.py. runtime.yaml. model_artifact_validate.py.

You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which three interfaces could you use to save the model artifact into the model catalog?. Git CLI. Console. OCI Python SDK. Oracle Cloud Infrastructure (OCI) Command Line Interface (CLI). Accelerated Data Science (ADS) Software Development Kit (SDK). Data Science Continuous Integration (ODSC) CLI.

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?. Weighted Recall. Explained Variance Score. Weighted Precision. Mean Absolute Error. F-1 Score.

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: Test accuracy is 80% and recall is 70%. Model 2: Test accuracy is 75% and recall is 85%. Which model would you prefer and why?. Model 2, because recall is high. Model 1, because the test accuracy is high. Model 2, because recall has more impact on predictions in this use case. Model 1, because recall has lesser impact on predictions in this use case.

What is the purpose of continuous training in MLOps?. To manually update software systems. To eliminate the need for data validation. To replace DevOps practices. To retrain machine learning models for redeployment.

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?. Pandas DataFrame. DOCX. JSON. Raw Images. XML.

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?. On the job run, set the environment variable that helps increase the size of the storage. 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. Your code is using too much disk space. Refactor the code to identify the problem.

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?. auto_transform(). suggest_recommendations(). visualize_transform(). sample().

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?. DataFrameLabelEncoder(). OneHotEncoder(). show_in_notebook(). auto_transform().

A team notices that their autoscaling system is making too many scaling adjustments in a short time frame, causing instability. What feature can help mitigate this issue?. Static resource allocation. Cooldown periods. Custom NQL expressions. Load balancer.

You are deploying a machine learning model on Oracle Data Science and decide to use metric-based autoscaling to manage resources efficiently. You set the autoscaling policy to trigger when CPU utilization exceeds 70% for five consecutive monitoring intervals. The cool-down period is set to 10 minutes. During peak usage, the CPU utilization hits 75% for six consecutive intervals, triggering the autoscaling event. What will happen immediately after the autoscaling event is triggered?. The system will immediately trigger another autoscaling event if CPU utilization exceeds 70%. The model deployment will return to its original size after the cool-down period. The cool-down period will prevent any performance metrics from being evaluated. The cool-down period will begin, and no further autoscaling events will be triggered for 10 minutes.