01. Which OCI service enables you to build, train, and deploy machine learning models in the cloud?
a) Oracle Cloud Infrastructure Data Catalog
b) Oracle Cloud Infrastructure Data Integration
c) Oracle Cloud Infrastructure Data Science
d) Oracle Cloud Infrastructure Data Flow
02. 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 a new job every time you need to run your code and pass the parameters as en-vironment variables.
b) Create your code to expect different parameters as command line arguments, and create it new job every time you run the code.
c) Create a new no by setting the required parameters in your code, and create a new job for mery code change.
d) 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.
03. You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model.
Which of the following TWO prevailing metrics would you use for evaluating multiclass classification model?
a) Recall
b) Mean squared error
c) F1 Score
d) R-Squared
e) Explained variance score
04. How is the storage associated with OCI Data Science Workspace managed?
a) Data is stored on local disk within the workspace instance.
b) Data is automatically stored in an attached Object Storage bucket.
c) Data is stored in a separate File Storage service.
d) Data is stored in an external database using block volumes.
05. Which technique can be used for feature engineering in the machine learning lifecycle?
a) Principal Component Analysis (PCA)
b) K-means clustering
c) Support Vector Machines (SVM)
d) Gradient boosting
06. What does MLOps stand for?
a) Machine Learning Optimization Strategies
b) Model Lifecycle Operations
c) Machine Learning Operations
d) Managed Learning Operations
07. How can you collaborate with team members in OCI Data Science Workspace?
a) By granting access to specific notebooks and files
b) By using version control systems integrated with the workspace
c) By sharing the workspace instance with other users
d) By enabling chat and video conferencing within the workspace
08. Where do calls to stdout and stderr from score.py go in a model deployment?
a) The file that was defined for them on the Virtual stachine (VM).
b) The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment.
c) The OCI Cloud Shell, which can be accessed from the console.
d) The OCI console.
09. Which OCI service provides a managed Kubernetes service for deploying, scaling, and managing containerized applications?
a) Oracle Cloud Infrastructure Container Registry
b) Oracle Cloud Infrastructure Load Balancing
c) Oracle Cloud Infrastructure Container Engine for Kubernetes
d) Oracle Cloud Infrastructure Streaming
10. What preparation steps are required to access an Oracle AI service SDK from a Data Science notebook session?
a) Call the Accented Data Science (ADS) command to enable Al integration
b) Create and upload the API signing key and config file
c) Import the REST API
d) Create and upload execute.py and runtime.yaml
The Oracle Cloud Infrastructure Data Science Professional (1Z0-1110-23) Sample Question Set is designed to help you prepare for the Oracle Cloud Infrastructure 2023 Certified Data Science Professional certification exam. To become familiar with the actual Oracle Certification exam environment, we suggest you try our Sample Oracle 1Z0-1110-23 Certification Practice Exam.