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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	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 3	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 4	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.
Topic 5	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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q14-Q19):

NEW QUESTION # 14
True or false? Bias is a common problem in data science applications.

A. True
B. False

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Assess if bias is a common issue in data science.
* Define Bias: Systematic errors in data/models (e.g., skewed training data).
* Evaluate Statement:
* Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws-widely recognized in ML.
* Examples: Gender bias in hiring models, racial bias in facial recognition.
* Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.
* Conclusion: A (True) is correct.
OCI documentation notes: "Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks." This aligns with industry standards-bias is a well-documented issue, making A true.
Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".

NEW QUESTION # 15
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets from reference libraries and index websites such as scikit-learn?

A. DatasetFactory
B. SecretKeeper
C. DatasetBrowser
D. DataLabeling

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify ADS class for dataset access (e.g., scikit-learn).
* Evaluate Options:
* A: DataLabeling-Not an ADS class.
* B: DatasetBrowser-Not real.
* C: SecretKeeper-Credentials, not data.
* D: DatasetFactory-Loads datasets (e.g., open())-correct.
* Reasoning: DatasetFactory simplifies library dataset access.
* Conclusion: D is correct.
OCI documentation states: "DatasetFactory (D) in ADS SDK accesses datasets from libraries like scikit-learn (e.g., DatasetFactory.open('sklearn.datasets:load_iris'))." A, B, and C don't exist or apply-only D fits.
Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".

NEW QUESTION # 16
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?

A. Moving the changes into staging area for the next commit
B. Pushing changes to a remote repository
C. Converting an existing local project folder to a Git repository
D. Updating the local repo to match the content from a remote repository
E. Making a commit that is taking a snapshot of the local repository for the next push

Answer: B,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Analyze Git Operations: Identify which depend on internet speed.
* Evaluate Options:
* A. Staging (git add): Local operation-adds files to the index; no network involved.
* B. Updating local repo (git pull): Downloads remote changes-requires internet, slowed by poor connectivity.
* C. Pushing changes (git push): Uploads local commits to remote-network-dependent, delayed by slow speed.
* D. Committing (git commit): Local snapshot-no network needed.
* E. Converting to Git repo (git init): Local initialization-no internet required.
* Reasoning: Only B and C involve network transfers, directly impacted by slow internet.
* Conclusion: B and C are the correct choices.
Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as "bandwidth-sensitive" in OCI's guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user's machine, unaffected by internet speed. This matches standard Git behavior and OCI's implementation.
Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".

NEW QUESTION # 17
As a data scientist, you require a pipeline to train ML models. When can a pipeline run be initiated?

A. Pipeline can be initiated after the active state.
B. Pipeline can be initiated during the pipeline run state.
C. Pipeline can be initiated before the active state.
D. Pipeline can be initiated once it is created.

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine when an OCI Data Science pipeline can start.
* Understand Pipelines: They're workflows with defined steps, executed on demand or scheduled.
* Evaluate Options:
* A: Once created, a pipeline can be run immediately-correct.
* B: "During run state" implies it's already running-illogical.
* C: "After active state" is unclear; pipelines run when triggered, not post-state.
* D: "Before active state" is vague-creation precedes running.
* Reasoning: Pipelines are executable post-creation via UI/CLI-simplest interpretation is A.
* Conclusion: A is correct.
OCI Data Science documentation states: "After a pipeline is created, you can initiate a pipeline run immediately or schedule it using the OCI Console, CLI, or SDK." B, C, and D misalign with this-running starts post-creation (A), not during/after ambiguous states.
Oracle Cloud Infrastructure Data Science Documentation, "Pipelines - Running a Pipeline".

NEW QUESTION # 18
What is the name of the machine learning library used in Apache Spark?

A. MLib
B. GraphX
C. HadoopML
D. Structured Streaming

Answer: A

Explanation:
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".

NEW QUESTION # 19
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