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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

Topic 1

Create and Manage Projects and Notebook Sessions: This part assesses the skills of Cloud Data Scientists and focuses on setting up and managing projects and notebook sessions within OCI Data Science. It also covers managing Conda environments, integrating OCI Vault for credentials, using Git-based repositories for source code control, and organizing your development environment to support streamlined collaboration and reproducibility.

Topic 2

Apply MLOps Practices: This domain targets the skills of Cloud Data Scientists and focuses on applying MLOps within the OCI ecosystem. It covers the architecture of OCI MLOps, managing custom jobs, leveraging autoscaling for deployed models, monitoring, logging, and automating ML workflows using pipelines to ensure scalable and production-ready deployments.

Topic 3

Use Related OCI Services: This final section measures the competence of Machine Learning Engineers in utilizing OCI-integrated services to enhance data science capabilities. It includes creating Spark applications through OCI Data Flow, utilizing the OCI Open Data Service, and integrating other tools to optimize data handling and model execution workflows.

Topic 4

OCI Data Science - Introduction & Configuration: This section of the exam measures the skills of Machine Learning Engineers and covers foundational concepts of Oracle Cloud Infrastructure (OCI) Data Science. It includes an overview of the platform, its architecture, and the capabilities offered by the Accelerated Data Science (ADS) SDK. It also addresses the initial configuration of tenancy and workspace setup to begin data science operations in OCI.

Topic 5

Implement End-to-End Machine Learning Lifecycle: This section evaluates the abilities of Machine Learning Engineers and includes an end-to-end walkthrough of the ML lifecycle within OCI. It involves data acquisition from various sources, data preparation, visualization, profiling, model building with open-source libraries, Oracle AutoML, model evaluation, interpretability with global and local explanations, and deployment using the model catalog.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q143-Q148):

NEW QUESTION # 143
Which technique can be used for feature engineering in the machine learning lifecycle?

A. Support Vector Machines (SVM)
B. K-means clustering
C. Gradient boosting
D. Principal Component Analysis (PCA)

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a feature engineering technique in ML.
* Understand Feature Engineering: Transforms raw data into model-ready features.
* Evaluate Options:
* A. PCA: Reduces dimensionality-feature engineering-correct.
* B. K-means: Clustering model-not feature engineering.
* C. SVM: Classification model-not feature engineering.
* D. Gradient boosting: Model training-not feature engineering.
* Reasoning: PCA creates new features via transformation-fits definition.
* Conclusion: A is correct.
OCI documentation states: "Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance." B, C, and D are modeling techniques-only A aligns with OCI's feature engineering stage.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".

NEW QUESTION # 144
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 archive.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,D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three required params for an OCI Data Flow app config.
* Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.
* Evaluate Options:
* A: Archive path-Optional if script is in Object Storage-incorrect.
* B: Local script path-Not needed; script is uploaded-incorrect.
* C: Compartment-Required for resource scope-correct.
* D: Bucket-Required for script storage/access-correct.
* E: Display name-Required for app identification-correct.
* Reasoning: C, D, E are mandatory metadata for Data Flow creation-script location is specified via bucket.
* Conclusion: C, D, E are correct.
OCI documentation states: "To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object." Local paths (B) or archives (A) are optional or handled separately-only C, D, E are required per OCI's Data Flow API spec.
Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".

NEW QUESTION # 145
You're going to create an Oracle Cloud Infrastructure Anomaly Detection model for multivariate data. Where do you need to store the training data?

A. Object Storage Bucket
B. MySQL database
C. Autonomous Data Warehouse
D. Your local machine

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Anomaly Detection: This service trains models to detect anomalies in multivariate data (e.g., sensor readings), requiring data to be accessible within OCI's ecosystem.
* Assess Storage Requirements: The training data must be in a scalable, OCI-compatible location that the Anomaly Detection service can access programmatically.
* Evaluate Options:
* A. Your local machine: Data on a local machine isn't directly accessible to OCI services without upload, making it impractical for cloud-based training.
* B. MySQL database: While OCI supports MySQL, Anomaly Detection doesn't natively integrate with it for training data; it prefers file-based input.
* C. Autonomous Data Warehouse: This is a database for analytics, not the default storage for Anomaly Detection training data, which expects CSV/JSON files.
* D. Object Storage Bucket: OCI Object Storage is a scalable, durable storage service that Anomaly Detection uses to ingest training data (e.g., CSV files).
* Reasoning: Object Storage is the standard for large-scale data in OCI services, offering seamless integration with Anomaly Detection via APIs or SDKs.
* Conclusion: D is the correct choice as it aligns with the service's architecture.
The OCI Anomaly Detection service requires training data to be uploaded to an Object Storage Bucket in formats like CSV or JSON. This is explicitly outlined in the official documentation, which states that users must "upload the training dataset to an OCI Object Storage bucket" before creating a data asset for model training. Local storage (A) isn't viable for cloud processing, and databases like MySQL (B) or Autonomous Data Warehouse (C) aren't supported as primary inputs. Object Storage (D) provides the scalability and accessibility needed for multivariate anomaly detection workflows.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "Preparing Training Data" section.

NEW QUESTION # 146
You have just received a new dataset from a colleague. You want to quickly find out summary information about the dataset, 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_in_notebook()
B. show_corr()
C. to_xgb()
D. compute()

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Get summary info from an ADSDataset object.
* Evaluate Options:
* A: Correlation matrix-Specific, not full summary.
* B: Converts to XGBoost-Not for summary.
* C: Executes computation-Not summary-focused.
* D: Displays summary (types, counts, dist)-correct.
* Reasoning: show_in_notebook() provides a comprehensive overview.
* Conclusion: D is correct.
OCI documentation states: "show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook." A is partial, B and C are unrelated-only D meets the need per ADS SDK.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".

NEW QUESTION # 147
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?

A. Use command-line variables
B. Configure a compute shape
C. Use environmental variables
D. Configure a block volume

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Configure conda env for a pipeline step.
* Evaluate Options:
* A: Shape-Infra, not env config.
* B: Volume-Storage, not env.
* C: Command-line-Step args, not env.
* D: Env variables-Sets conda path-correct.
* Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).
* Conclusion: D is correct.
OCI documentation states: "Configure a pipeline step's conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition." A, B, and C address other aspects-only D fits env config.
Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".

NEW QUESTION # 148
......

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