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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: Design and Set Up Data Science Workspace	- Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects - Manage notebook sessions and compute resources
Topic 2: Apply MLOps Practices	- Implement model lifecycle management - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models
Topic 3: Use Related OCI Services	- Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services
Topic 4: Implement End-to-End Machine Learning Lifecycle	- Save and manage models using Model Catalog - Automate machine learning workflows and pipelines - Prepare and manage datasets - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models
Topic 5: OCI Data Science - Introduction and Configuration	- Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture

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Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q38-Q43):

NEW QUESTION # 38
What is the name of the machine learning library used in Apache Spark?

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

Answer: C

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.
1: Oracle Cloud Infrastructure Data Flow Documentation, "Apache Spark MLlib".

NEW QUESTION # 39
As you are working in your notebook session, you find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?

A. Ensure your files and environments are written to the block volume storage under the /home/datascience directory, deactivate the notebook session, and activate the notebook session with a larger compute shape selected
B. Deactivate your notebook session, provision a new notebook session on a larger compute shapeand re-create all of your file changes
C. Download all your files and data to your local machine, delete your notebook session, provision a new notebook session on a larger compute shape, and upload your files from your local machine to the new notebook session
D. Create a temporary bucket on Object Storage, write all your files and data to Object Storage, delete your notebook session, provision a new notebook session on a larger compute shape, and copy your files and data from your temporary bucket onto your new notebook session

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Scale up notebook without losing work.
Evaluate Options:
A: Object Storage—Extra steps, inefficient.
B: Block volume—Persists data, seamless scale—correct.
C: Local machine—Risky, cumbersome.
D: Recreate—Loses work, impractical.
Reasoning: B uses OCI’s built-in persistence.
Conclusion: B is correct.
OCI documentation states: “Files in /home/datascience (B) persist on block volume; deactivate, then reactivate with a larger shape to scale up without data loss.” A, C, and D add complexity or risk—only B is optimal per OCI’s design.
1: Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".

NEW QUESTION # 40
Which is NOT a valid OCI Data Science notebook session approach?

A. Avoid having multiple users in the same notebook session due to the possibility of resource contention and write conflicts
B. Ensure you don’t execute long-running Python processes in a notebook cell
C. Run the process directly in the terminal and use Python logging to get updates on the progress of your job
D. 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
E. 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 approach

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify an invalid notebook session practice.
Evaluate Options:
A: Valid—Long runs should use Jobs, not notebooks.
B: Valid—Terminal runs with logging are supported.
C: Valid—Multi-user conflicts are a concern.
D: Invalid—Copying from Object Storage to block volume is unnecessary; direct access is best.
E: Valid—Resource principals are secure and recommended.
Reasoning: D contradicts OCI’s direct-access efficiency.
Conclusion: D is incorrect.
OCI documentation advises: “Access data in Object Storage directly from notebook sessions using SDKs or resource principals—avoid local copies (D) unless necessary.” A, B, C, and E are best practices—D is inefficient and not standard.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Best Practices".

NEW QUESTION # 41
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor dat a. How does the required False Alarm Probability setting affect an anomaly detection model?

A. It determines how many false alarms occur before an error message is generated
B. It adds a score to each signal indicating the probability that it’s a false alarm
C. It changes the sensitivity of the model to detecting anomalies
D. It is used to disable the reporting of false alarms

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Understand the effect of False Alarm Probability (FAP) in OCI Anomaly Detection.
Understand FAP: Controls false positive rate—threshold for anomaly flagging.
Evaluate Options:
A: Disable reporting—Incorrect; FAP sets sensitivity, not on/off.
B: Changes sensitivity—Correct; lower FAP = fewer false positives—correct.
C: Count-based error—Incorrect; not a counter.
D: Score per signal—Incorrect; FAP is a global setting.
Reasoning: FAP adjusts detection threshold—direct impact on sensitivity.
Conclusion: B is correct.
OCI documentation states: “False Alarm Probability (FAP) (B) adjusts the model’s sensitivity in Anomaly Detection—lower values increase specificity, reducing false positives.” A, C, and D misinterpret FAP’s role—only B aligns with OCI’s anomaly detection tuning.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Settings".

NEW QUESTION # 42
Which statement about resource principals is true?

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

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.
Evaluate Options:
A: False—Resource principals eliminate manual credential management.
B: False—They’re secure, leveraging IAM policies, not less secure than API keys.
C: False—Data Science supports resource principals for accessing resources (e.g., Object Storage).
D: True—Resource principals are an IAM feature authorizing resources as actors.
Reasoning: D captures the essence of resource principals as an IAM mechanism.
Conclusion: D is correct.
OCI documentation states: “A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies.” This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.
1: Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".

NEW QUESTION # 43
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