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Each question shows the correct answer and an explanation of why it is right
What is the name of the machine learning library used in Apache Spark?
Detailed Answer in Step-by-Step Solution:
Objective: Identify Apache Spark's ML library.
Understand Spark: A big data framework with specialized libraries.
Evaluate Options:
A: MLib (correctly MLlib)---Spark's machine learning library.
B: GraphX---Graph processing, not ML.
C: Structured Streaming---Streaming data, not ML.
D: HadoopML---Not a Spark library (Hadoop-related).
Reasoning: MLlib is Spark's official ML toolkit (e.g., regression, clustering).
Conclusion: A is correct (noting ''MLib'' should be ''MLlib'').
OCI Data Science supports Spark via Data Flow, where ''MLlib (Machine Learning library) provides scalable ML algorithms.'' GraphX (B) and Structured Streaming (C) serve other purposes, and HadoopML (D) isn't real---MLlib (A) is the standard, despite the typo.
: Oracle Cloud Infrastructure Data Flow Documentation, 'Apache Spark MLlib'.
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?
Detailed Answer in Step-by-Step Solution:
Objective: Publish a conda env to Object Storage.
Process: Initialize bucket config, then publish env.
Evaluate Options:
A: Publishes env with slug---correct final step.
B: Lists envs---unrelated to publishing.
C: Sets bucket details---required setup---correct.
D: Creates env---not publishing.
E: Activates env---not for sharing.
Reasoning: C sets up, A executes---standard workflow.
Conclusion: A and C are correct.
OCI documentation states: ''To publish a conda environment, first run odsc conda init (C) with bucket namespace and name, then odsc conda publish (A) with a slug to upload to Object Storage.'' B, D, and E serve other purposes---only A and C are required per OCI's process.
: Oracle Cloud Infrastructure Data Science CLI Reference, 'Publishing Conda Environments'.
You want to make your model more parsimonious to reduce the cost of collecting and processing dat
a. You plan to do this by removing features that are highly correlated. You would like to create a heatmap 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?
Detailed Answer in Step-by-Step Solution:
Objective: Visualize correlation between continuous and categorical features using ADS SDK.
Understand Correlation Types:
Continuous vs. Continuous: Pearson correlation.
Categorical vs. Categorical: Cramer's V.
Continuous vs. Categorical: Correlation ratio (eta).
Evaluate Options:
A . corr(): General correlation (Pearson), not suited for mixed types---incorrect.
B . correlation_ratio_plot(): Plots correlation ratio for continuous-categorical---correct.
C . pearson_plot(): Not an ADS method; Pearson is continuous-only---incorrect.
D . cramersv_plot(): Cramer's V for categorical-categorical---incorrect.
Reasoning: Correlation ratio measures association between continuous and categorical variables---ideal for heatmap in this mixed scenario.
Conclusion: B is correct.
OCI documentation states: ''The correlation_ratio_plot() method (B) in ADS SDK generates a heatmap displaying the correlation ratio between continuous and categorical features, suitable for identifying highly correlated features for removal.'' corr() (A) defaults to Pearson, pearson_plot() (C) isn't real, and cramersv_plot() (D) is for categorical pairs---only B aligns with OCI's ADS capabilities for this use case.
: Oracle Cloud Infrastructure ADS SDK Documentation, 'Correlation Visualization Methods'.
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.
Detailed Answer in Step-by-Step Solution:
Objective: Identify two reasons for key rotation in OCI Vault.
Understand Key Rotation: Enhances security by updating keys.
Evaluate Options:
A: Five-key limit---False, no such restriction.
B: Efficiency---False, not the purpose.
C: Reuse---False, rotation prevents reuse.
D: Reduces risk---True, limits exposure---correct.
E: Limits data---True, reduces breach scope---correct.
Reasoning: D and E are security-focused---key Vault benefits.
Conclusion: D and E are correct.
OCI documentation states: ''Key rotation in Vault (D) reduces risk if a key is compromised and (E) limits the data encrypted by a single key version, enhancing security.'' A, B, and C misrepresent rotation's purpose---only D and E align with OCI's Vault best practices.
: Oracle Cloud Infrastructure Vault Documentation, 'Key Rotation Benefits'.
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?
Detailed Answer in Step-by-Step Solution:
Objective: Identify interfaces to save a 2 GB model to the Model Catalog.
Evaluate Options:
A: OCI CLI---Supports Data Science tasks---possible but not primary.
B: ADS SDK---Designed for model catalog ops---correct.
C: ODSC CLI---Not standard; likely typo for OCI CLI.
D: Console---GUI for catalog uploads---correct.
E: OCI Python SDK---Programmatic catalog access---correct.
F: Git CLI---Version control, not catalog-related.
Reasoning: B, D, E are OCI's primary interfaces; A is valid but less emphasized.
Conclusion: B, D, E are correct (A plausible but not top-tier).
OCI documentation lists ''ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog.'' OCI CLI (A) works but isn't highlighted, C isn't real, and F is unrelated---B, D, E are the standard trio.
: Oracle Cloud Infrastructure Data Science Documentation, 'Model Catalog Interfaces'.
158 questions covering all exam domains, starting from $20
Exam domains verified against: Official Oracle 1Z0-1110-25 exam guide, last checked September 2026.
Understand the OCI Data Science platform capabilities and the Accelerated Data Science (ADS) SDK features for data science workflows. Configure your tenancy with proper access controls and initialization steps needed to start working with data science projects.
Sample question from this domain above: Q4
Create Projects and Notebook sessions for your team to collaborate on experiments. Set up Conda environments for dependency management, use OCI Vault for secure credential storage, and configure Git repositories for version control of your code.
Guide data from ingestion through exploration, preparation, visualization and profiling to model creation using both OCI services and open source libraries. Deploy models through the Model Catalog and integrate with OCI Generative AI, LangChain, and AI Quick Actions for production inference.
Understand OCI MLOps architecture and create Jobs to automate custom tasks and model retraining workflows. Use Pipelines to orchestrate machine learning processes, scale model deployments with autoscaling for inference, and monitor system performance with logging and metrics.
Create and manage Spark applications through OCI Data Flow integrated with Data Science. Explore OCI Open Data Service for public datasets and use OCI Data Labeling to prepare and export annotated datasets for model training.
Common questions about the exam itself
