Oracle Data Science Professional (1Z0-1110-24) Exam Questions 2024
Oracle Data Science Professional (1Z0-1110-24) Exam Questions 2024
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Question 1:
You ‘re building a machine learning model that requires a large, publicly available dataset for training. Which OCI service can you leverage to access such datasets?
(a) Oracle Cloud Infrastructure Data Integration
(b) Oracle Cloud Infrastructure Data Flow
(c) **Oracle Cloud Infrastructure Open Data Service**
(d) Oracle Cloud Infrastructure Data Science Service
A. D
B. A
C. C
D. B
Answer: C
Explanation:
c) Oracle Cloud Infrastructure Open Data Service
Here‘s why the other options are not the best fit:
a) Oracle Cloud Infrastructure Data Integration: While this service helps move and transform data, it doesn‘t have functionalities to specifically browse or access publicly available datasets.
b) Oracle Cloud Infrastructure Data Flow: Similar to Data Integration, Data Flow focuses on data processing pipelines and doesn ‘t have built-in capabilities for exploring or accessing public datasets.
d) Oracle Cloud Infrastructure Data Science Service: While primarily dedicated to building and managing ML models, it doesn ‘t directly store or provide access to public datasets within the service itself.
Oracle Cloud Infrastructure Open Data Service (ODS), on the other hand, offers:
Public datasets: A searchable catalog of various publicly available datasets from sources like government agencies, institutions, and organizations.
Direct access: Download or stream datasets directly through the service or integrate them into data pipelines using tools like Data Flow.
Data discovery: Filter and search datasets by categories, keywords, and attributes to find relevant ones for your machine learning project.
Governance and security: Applies access control and encryption to ensure data security and compliance.
Question 2:
Which of the following is NOT a valid method for deploying a trained machine learning model in Oracle Cloud Infrastructure Data Science Service?
A) Creating a Model Image based on your trained model and deploying it on an Oracle Container Engine Kubernetes cluster.
B) Packaging your model and associated scripts into a Model Archive (MAR) file and registering it in the Model Catalog.
C) Utilizing Oracle AutoML‘s deployment functionality to seamlessly deploy your automatically generated model.
D) Saving your model code as a Jupyter Notebook and sharing it with other users via the Code Repository.
A. D
B. A
C. B
D. C
Answer: A
Explanation:
D) Saving your model code as a Jupyter Notebook and sharing it with other users via the Code Repository.
Here‘s why the other options are valid deployment methods:
A) Creating a Model Image: This is a popular approach where you package your trained model and dependencies into a container image and deploy it on Kubernetes.
B) Packaging your model as a MAR file: The Model Archive format (MAR) is designed specifically for deployment within OCI Data Science Service. It simplifies deployment by packaging model artifacts and configuration together.
C) Using Oracle AutoML‘s deployment functionality: AutoML automates parts of the machine learning lifecycle, including deployment. This functionality allows you to seamlessly deploy your generated model with a few clicks.
While sharing the model code through the Code Repository might seem like a deployment method, it‘s not considered a formal deployment within the service. The code itself doesn‘t represent a ready-to-use model. For production-ready deployment, you need to package your model in a way that can be executed and served predictions without relying on the specific notebook environment.
Question 3:
What component within the OCI MLOps architecture is responsible for orchestrating and scheduling model training and deployment pipelines?
A. Oracle Data Integration (ODI)
B. Oracle Cloud Infrastructure Data Flow (OCID)
C. Oracle Autonomous Database (ADB)
D. Oracle Machine Learning for Enterprises (OMLE)
A. C
B. A
C. D
D. B
Answer: D
Explanation:
B) Oracle Cloud Infrastructure Data Flow (OCID)
Here‘s why the other options aren‘t suited for this role:
A) Oracle Data Integration (ODI): While ODI offers data integration capabilities, it lacks the specific features for building and scheduling complex ML pipelines.
C) Oracle Autonomous Database (ADB): ADB focuses on providing a self-managing database environment and doesn‘t have built-in functionalities for ML pipeline orchestration.
D) Oracle Machine Learning for Enterprises (OMLE): OMLE is a platform for enterprise-grade ML workloads but doesn‘t specifically handle pipeline orchestration and scheduling in the OCI MLOps architecture.
OCID, on the other hand, is a serverless, managed data flow service within OCI specifically designed for building and executing data pipelines. It offers features like:
Visual and code-based workflow creation: You can build pipelines using a drag-and-drop interface or write custom code.
Scheduling triggers: Set up schedules or event-based triggers to automatically execute pipelines.
Integration with other OCI services: OCID seamlessly integrates with Data Science Service, Object Storage, and other OCI services for comprehensive data handling and model deployment.
Question 4:
Main drawback of relying solely on accuracy as a model evaluation metric for a binary classification problem with imbalanced classes?
A. It doesn‘t capture the trade-off between true positives and false positives.
B. It is computationally expensive to calculate.
C. It cannot be used with multi-class classification problems.
D. It requires manual interpretation of threshold values.
A. A
B. D
C. B
D. C
Answer: A
Explanation:
A. It doesn‘t capture the trade-off between true positives and false positives.
Here‘s why accuracy isn‘t ideal in this scenario:
Imbalanced classes: When one class significantly outnumbers the other (e.g., 90% negative examples, 10% positive examples), a model can simply predict the majority class (negative) most of the time and achieve high accuracy (90%). However, this completely ignores the minority class (positive) and provides no meaningful information about the model‘s ability to correctly identify rare but important events.
Focus on true positives: In many real-world applications, identifying the positive class correctly is crucial, even if it means accepting some false positives. For example, in fraud detection, missing a fraudulent transaction (false negative) can be much more costly than mistakenly flagging a genuine transaction (false positive).
Therefore, relying solely on accuracy in such cases can be misleading and lead to poorly performing models that prioritize the majority class at the expense of the minority class. Alternative metrics like:
Precision: Measures the proportion of predicted positive cases that are truly positive.
Recall: Measures the proportion of actual positive cases that are correctly identified.
F1-score: Combines precision and recall to provide a balanced view of performance.
Question 5:
Which feature of OCI Open Data Service ensures the quality and reliability of datasets?
(a) Version control
(b) Data lineage tracking
(c) Access control lists (ACLs)
(d) Encryption
A. B
B. D
C. C
D. A
Answer: A
Explanation:
b) Data lineage tracking
Here‘s why:
Version control: While important for maintaining historical versions of datasets, it doesn‘t directly indicate the quality or reliability of a specific version.
Access control lists (ACLs): These primarily provide security by controlling access to datasets, not directly addressing quality or reliability.
Encryption: Although essential for data security, it doesn‘t offer insights into the dataset‘s content and potential quality issues.
Data lineage tracking in ODS serves a crucial purpose for dataset quality and reliability:
Transparency: Tracks the origin and transformations applied to a dataset, revealing its provenance and the processes it has undergone.
Quality checks: By understanding the processing steps, you can identify potential issues introduced during transformations and assess the overall quality of the data.
Reproducibility: Makes it easier to reproduce analyses and results by tracing the exact data used and processing steps involved.
Issue resolution: Helps troubleshoot anomalies or errors by pinpoint-ing the location where issues might have been introduced.
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Question 6:
Which of the following best practices is NOT recommended for logging within OCI Data Science pipelines?
a) Including timestamps and relevant context information in logs.
b) Using structured logging formats for easy analysis.
c) Logging sensitive information like passwords or API keys.
d) Setting appropriate log retention policies based on compliance requirements.
A. C
B. B
C. A
D. D
Answer: A
Explanation:
c) Logging sensitive information like passwords or API keys.
Here‘s why:
a) Including timestamps and context information: This is crucial for understanding the timing and context of events within your pipeline, making troubleshooting and analysis easier.
b) Using structured logging formats: Structured formats like JSON or CSV provide well-defined fields for log data, facilitating parsing and analysis with tools and dashboards.
c) Logging sensitive information: Including passwords, API keys, or other credentials in logs poses a significant security risk if accidentally exposed. Always use secure storage mechanisms and avoid logging such sensitive data.
d) Setting appropriate log retention policies: Defining retention policies based on compliance requirements and data lifecycle needs ensures efficient storage management and avoids unnecessary data accumulation.
Question 7:
What feature within OCI Data Science Workspace helps manage concurrent Notebook sessions effectively?
A. Version control directly within Notebooks.
B. Collaboration tools like chat or video conferencing integrated into Notebooks.
C. Automatic locking mechanisms for Notebooks when edited by others.
D. Scheduled execution of Notebooks through job automation tools.
A. A
B. C
C. D
D. B
Answer: B
Explanation:
C. Automatic locking mechanisms for Notebooks when edited by others.
Here‘s why:
Version control: While important for tracking changes, it doesn‘t directly address concurrent access conflicts.
Collaboration tools: Useful for communication, but don‘t prevent accidental overwrites.
Scheduled execution: Useful for automation, but doesn‘t address real-time collaboration issues.
Automatic locking: This feature ensures that only one user can edit a Notebook at a time, preventing conflicts and data loss. When another user attempts to edit a locked Notebook, they are notified and can wait for the lock to be released or choose a different Notebook.
Therefore, automatic locking mechanisms provide the most direct and effective way to manage concurrent access and avoid conflicts in OCI Data Science Workspace.
Question 8:
In Oracle Data Explorer (ODE), which feature allows you to interactively explore relationships between numerical variables using visual scatter plots and correlation matrices?
A. Data lineage
B. Data profiling
C. Data enrichment
D. Visual analysis
A. B
B. A
C. C
D. D
Answer: D
Explanation:
D. Visual analysis
Here‘s why the other options are not as relevant:
A. Data lineage: This feature tracks the origin and transformation of data, not for interactive exploration of relationships.
B. Data profiling: While data profiling provides summary statistics, it typically doesn‘t include interactive visualization capabilities.
C. Data enrichment: This feature focuses on adding additional information to the data, not analyzing relationships between existing variables.
The Visual Analysis feature in ODE offers various tools for exploring data visually:
Scatter plots: You can create scatter plots to visualize the relationship between two numerical variables, allowing you to identify potential linear or non-linear relationships and outliers.
Correlation matrices: This tool displays the correlation coefficients between all pairs of numerical variables, providing a heatmap overview of their linear relationships.
Histograms and box plots: These visualizations help understand the distribution of individual numerical variables.
Filtering and drill-down: You can interactively filter data points based on specific criteria and drill down into specific areas of the visualizations for deeper exploration.
Question 9:
In a productionized OCI MLOps environment, how is model performance and potential drift monitored?
A. By manually running evaluation scripts in ODSS notebooks on a regular basis.
B. Using pre-defined metrics available within the Data Science workspace itself.
C. Through custom dashboards created with Oracle Analytics Cloud (OAC).
D. Utilizing built-in monitoring capabilities of Oracle AutoML models.
A. B
B. D
C. C
D. A
Answer: C
Explanation:
C. Through custom dashboards created with Oracle Analytics Cloud (OAC).
Here‘s why the other options have limitations:
A. Manually running evaluation scripts: This is resource-intensive, potentially error-prone, and doesn‘t offer continuous monitoring.
B. Pre-defined metrics: While the Data Science workspace might offer some basic metrics, it might not cover all necessary aspects of performance and drift detection.
D. AutoML built-in monitoring: This applies only to models generated by Oracle AutoML and might not be comprehensive enough for custom models.
OAC empowers you to create custom dashboards:
Ingesting various data sources: Integrate performance metrics, data drift scores, logs, and other relevant data from Data Science Service, Monitoring, and other OCI services.
Visualizing key performance indicators (KPIs): Create dashboards displaying metrics like accuracy, precision, recall, F1 score, and latency, tailored to your specific model and business needs.
Detecting drift visually and through alerts: Track changes in these metrics over time and set up alerts to notify you when potential drift occurs.
Performing root cause analysis: Drill down into specific data points and logs to understand the reasons behind drifts and performance issues.
Question 10:
When using containerized models with OCI Data Science, what service allows managing and scaling deployments?
A. Oracle Cloud Infrastructure Registry (OCI Registry)
B. Oracle Container Engine for Kubernetes (OKE)
C. Oracle Data Science Model Management (DSMM)
D. Oracle Data Catalog (ODC)
A. B
B. C
C. A
D. D
Answer: A
Explanation:
B. Oracle Container Engine for Kubernetes (OKE)
Here‘s why the other options are less appropriate:
A. Oracle Cloud Infrastructure Registry (OCI Registry): This service stores container images, but it doesn‘t handle deployment and scaling of applications, including models.
C. Oracle Data Science Model Management (DSMM): While DSMM manages models and deployments, it primarily focuses on non-containerized models served through HTTP endpoints. It doesn‘t manage containerized deployments.
D. Oracle Data Catalog (ODC): This service focuses on cataloging and tracking model metadata, not model deployment and scaling.
OKE is a managed Kubernetes platform within OCI, offering several advantages for containerized model deployments:
Flexible deployment: Package your model as a container image and deploy it to OKE, providing flexibility and consistency with other containerized applications.
Horizontal scaling: Automatically scale your model deployment based on traffic or resource usage, ensuring optimal performance under varying loads.
High availability and fault tolerance: Leverage Kubernetes features for liveness probes, health checks, and automatic restarts to ensure your model remains available even if individual containers fail.
Integration with OCI services: OKE integrates with other OCI services like Data Science Service and Object Storage, facilitating data access and model management.
While other services play crucial roles in the OCI Data Science ecosystem, OKE‘s specialized functionalities for container orchestration and scaling make it the best choice for managing and scaling containerized model deployments.
For a full set of 150 questions. Go to
https://skillcertpro.com/product/oracle-data-science-professional-1z0-1110-24-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.