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NEW QUESTION # 21
Which statement is true about standards?
- A. They are the foundation of corporate governance
- B. They are methods and instructions on how to maintain or accomplish the directives of the policy
- C. They are the result of a regulation or contractual requirement or an industry requirement
- D. They may be audited
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a true statement about standards in an OCI context (likely governance/security).
* Understand Standards: Rules or benchmarks, often compliance-related.
* Evaluate Options:
* A: Auditable-True; standards are checked for adherence.
* B: Result of requirements-Partially true, but not always.
* C: Methods/instructions-More procedural, not defining standards.
* D: Foundation of governance-Broad, not specific to standards.
* Reasoning: A is universally true-standards face audits (e.g., SOC, ISO).
* Conclusion: A is correct.
OCI documentation notes: "Standards (e.g., security standards) may be audited (A) to ensure compliance with OCI policies or external regulations." B is a source, C describes procedures, D is too vague-only A is consistently true per OCI's compliance framework.
Oracle Cloud Infrastructure Security Documentation, "Compliance and Standards".
NEW QUESTION # 22
Which of these options allow the sharing and loading back of ML models into a notebook session?
- A. Model taxonomy
- B. Model catalog
- C. Model deployment
- D. Model provenance
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the mechanism for sharing and reloading ML models in OCI Data Science.
* Evaluate Options:
* A. Model provenance: Tracks model origin-informative but not a sharing mechanism.
* B. Model taxonomy: Categorizes models (e.g., regression)-not for sharing/loading.
* C. Model deployment: Makes models accessible as endpoints, not for notebook reloading.
* D. Model catalog: Stores models and artifacts, enabling sharing and loading into sessions.
* Reasoning: The Model Catalog is OCI's centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK).
* Conclusion: D is the correct tool.
The OCI Model Catalog "enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation." Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".
NEW QUESTION # 23
You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?
- A. Feature Permutation Importance Explanations
- B. Feature Dependence Explanations
- C. Accumulated Local Effects
- D. Local Interpretable Model-Agnostic Explanations
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an explanation technique for feature-target relationships with correlated features.
* Evaluate Options:
* A: Permutation-Breaks with high correlation.
* B: LIME-Local, not global relationships.
* C: Dependence-Not a standard term; vague.
* D: ALE-Handles correlation, shows feature effects-correct.
* Reasoning: ALE is robust to correlated features, ideal here.
* Conclusion: D is correct.
OCI documentation states: "Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation." B is local, C isn't defined-only D fits per OCI's explanation tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".
NEW QUESTION # 24
You want to write a program that performs document analysis tasks such as extracting text and tables from a document. Which Oracle AI service would you use?
- A. Oracle Digital Assistant
- B. OCI Speech
- C. OCI Vision
- D. OCI Language
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an OCI AI service for text and table extraction from documents.
* Evaluate Options:
* A: Language-Text analysis, not extraction-incorrect.
* B: Digital Assistant-Chatbots, not document tasks-incorrect.
* C: Speech-Audio transcription, not documents-incorrect.
* D: Vision-OCR for text/tables-correct.
* Reasoning: Vision's OCR extracts text and tables from document images.
* Conclusion: D is correct.
OCI documentation states: "OCI Vision (D) uses OCR to extract text and tables from documents, supporting document analysis tasks." A analyzes text post-extraction, B and C are unrelated-only D fits per OCI's AI services.
Oracle Cloud Infrastructure Vision Documentation, "Document Analysis Features".
NEW QUESTION # 25
Which OCI service provides a scalable environment for developers and data scientists to run Apache Spark applications at scale?
- A. Data Science
- B. Anomaly Detection
- C. Data Labeling
- D. Data Flow
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for scalable Spark applications.
* Evaluate Options:
* A: Data Science-ML platform, not Spark-focused.
* B: Anomaly Detection-Specific ML service, not general Spark.
* C: Data Labeling-Annotation tool, not Spark-related.
* D: Data Flow-Managed Spark service for big data.
* Reasoning: Data Flow is OCI's Spark execution engine.
* Conclusion: D is correct.
OCI Data Flow "provides a fully managed environment to run Apache Spark applications at scale, ideal for data processing and ML tasks." Data Science (A) supports Spark in notebooks, but Data Flow (D) is the dedicated, scalable solution-B and C are unrelated.
Oracle Cloud Infrastructure Data Flow Documentation, "Overview".
NEW QUESTION # 26
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?
- A. Data used for analysis can be text or numerical in nature
- B. It is trained on a combination of customer and general industry datasets
- C. It is an important tool for detecting fraud, network intrusions, and discrepancies in sensor time series analysis
- D. Accepted file types are SQL and Python
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find a true statement about OCI Anomaly Detection.
* Understand Service: Detects anomalies in multivariate data (e.g., time series).
* Evaluate Options:
* A: False-Accepted types are CSV/JSON, not SQL/Python.
* B: Partially true-Focuses on numerical data (e.g., sensors), not text broadly.
* C: True-Used for fraud, intrusions, and sensor anomalies (key use cases).
* D: False-Trained on customer data only, not general datasets.
* Reasoning: C aligns with documented applications; others misalign.
* Conclusion: C is correct.
OCI Anomaly Detection documentation states: "The service is designed to detect anomalies in time series data, making it valuable for fraud detection, network intrusion analysis, and sensor discrepancies." A is incorrect (file formats), B overgeneralizes (numerical focus), and D misstates training data-only C matches the service's purpose.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".
NEW QUESTION # 27
You are working as a data scientist for a healthcare company. They decided to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended steps to develop a PySpark application in OCI Data Science?
- A. Configure core-site.xml, install a PySpark conda environment, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application, launch a notebook session
- B. Launch a notebook session, install a PySpark conda environment, configure core-site.xml, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
- C. Install a Spark conda environment, configure core-site.xml, launch a notebook session, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application
- D. Launch a notebook session, configure core-site.xml, install a PySpark conda environment, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence steps for a PySpark app in OCI Data Science.
* Evaluate Steps:
* Launch notebook: First-provides the environment.
* Install PySpark conda: Second-sets up Spark libraries.
* Configure core-site.xml: Third-connects to data (e.g., Object Storage).
* Develop app: Fourth-writes the PySpark code.
* Data Flow: Fifth-optional scaling, post-development.
* Check Options: D (1, 2, 3, 4, 5) matches this logical flow.
* Reasoning: Notebook first, then setup, coding, and scaling.
* Conclusion: D is correct.
OCI documentation recommends: "1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale." D follows this-others (A, B, C) misorder critical steps like launching the notebook.
Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".
NEW QUESTION # 28
True or false? Data scientists typically need a combination of technical skills, nontechnical ones, and suitable personality traits to be successful.
- A. True
- B. False
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Assess required skills for data scientists.
* Analyze Skills:
* Technical: Coding, stats, ML.
* Nontechnical: Communication, business acumen.
* Traits: Curiosity, problem-solving.
* Reasoning: Success requires this mix-e.g., explaining models to stakeholders.
* Conclusion: A (True) is correct.
OCI documentation states: "Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking." This holistic requirement is true (A), not false (B).
Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".
NEW QUESTION # 29
You have built a machine model to predict whether a bank customer is going to default on a loan. You want to use Local Interpretable Model-Agnostic Explanations (LIME) to understand a specific prediction. What is the key idea behind LIME?
- A. Global behaviour of a machine learning model may be complex, while the local behaviour may be approximated with a simpler surrogate model
- B. Local explanation techniques are model-agnostic, while global explanation techniques are not
- C. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models
- D. Global and local behaviours of machine learning models are similar
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define LIME's core concept.
* Understand LIME: Explains individual predictions with local surrogate models.
* Evaluate Options:
* A: Complex global, simple local-Correct LIME principle.
* B: Agnosticism-True but not the key idea.
* C: Global/local similarity-False.
* D: Local vs. global agnosticism-Incorrect distinction.
* Reasoning: A captures LIME's local approximation focus.
* Conclusion: A is correct.
OCI documentation notes: "LIME (A) explains predictions by approximating complex global models with simpler local surrogate models around specific instances." B, C, and D misalign-only A reflects LIME's foundational idea per OCI's interpretability tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".
NEW QUESTION # 30
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?
- A. Oracle Digital Assistant
- B. OCI Speech
- C. OCI Vision
- D. OCI Language
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an OCI AI service to sort images by language.
* Evaluate Options:
* A: Digital Assistant-Chatbots, not image/language processing.
* B: Vision-Image analysis (e.g., object detection), not language sorting.
* C: Speech-Audio-to-text, not image-based.
* D: Language-Text analysis (e.g., language detection) after OCR-correct.
* Reasoning: Images need OCR (Vision) then language detection (Language)-D fits the sorting task.
* Conclusion: D is correct.
OCI Language "detects and classifies languages in text," often paired with OCI Vision's OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language-Digital Assistant (A) and Speech (C) don't apply. Documentation supports this workflow.
Oracle Cloud Infrastructure Language Documentation, "Language Detection".
NEW QUESTION # 31
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. SecretKeeper
- B. DataLabeling
- C. DatasetFactory
- D. DatasetBrowser
Answer: C
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 # 32
Which is NOT a valid OCI Data Science notebook session approach?
- A. 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
- B. Run the process directly in the terminal and use Python logging to get updates on the progress of your job
- C. Avoid having multiple users in the same notebook session due to the possibility of resource contention and write conflicts
- D. Ensure you don't execute long-running Python processes in a notebook cell
- 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: A
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.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Best Practices".
NEW QUESTION # 33
Six months ago, you created and deployed a model that predicts customer churn for a call centre. Initially, it was yielding quality predictions. However, over the last two months, users are questioning the credibility of the predictions. Which TWO methods would you employ to verify the accuracy of the model?
- A. Retrain the model
- B. Operational monitoring
- C. Redeploy the model
- D. Drift monitoring
- E. Validate the model using recent data
Answer: A,D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Address declining prediction accuracy and verify model performance.
* Analyze Problem: Degradation over time suggests data drift or model staleness-common ML issues.
* Evaluate Options:
* A. Retrain the model: Uses new data to update the model-fixes accuracy-correct.
* B. Validate with recent data: Tests performance but doesn't fix-diagnostic only.
* C. Drift monitoring: Detects data distribution shifts-verifies cause-correct.
* D. Redeploy the model: Repeats deployment, doesn't address root cause.
* E. Operational monitoring: Tracks infra (e.g., latency), not prediction accuracy.
* Reasoning: C identifies drift (why accuracy dropped), A corrects it-best pair for verification and improvement.
* Conclusion: A and C are correct.
OCI documentation states: "Drift monitoring (C) detects changes in data distribution that impact accuracy, while retraining (A) with new data restores model performance." Validation (B) checks but doesn't fix, redeployment (D) is redundant, and operational monitoring (E) is infra-focused-only A and C align with OCI's model maintenance strategy.
Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".
NEW QUESTION # 34
As a data scientist, you are working on a global health dataset that has data from more than 50 countries. You want to encode three features, such as 'countries', 'race', and 'body organ' as categories. Which option would you use to encode the categorical feature?
- A. DataFrameLabelEncode()
- B. auto_transform()
- C. OneHotEncoder()
- D. show_in_notebook()
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Encode categorical features in a Data Science context (likely ADS SDK).
* Understand Encoding: Converts categories (e.g., countries) to numerical forms.
* Evaluate Options:
* A: Not a standard ADS method-incorrect.
* B: General transformation, not specific encoding-incorrect.
* C: OneHotEncoder-Standard for categorical encoding-correct.
* D: Visualization, not encoding-incorrect.
* Reasoning: One-hot encoding creates binary columns-ideal for multiple categories.
* Conclusion: C is correct.
OCI documentation states: "In ADS SDK, use OneHotEncoder (C) from sklearn (or similar) to encode categorical features like 'countries' into binary vectors for modeling." A isn't real, B is too broad, D is unrelated-only C fits OCI's encoding practice.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Encoding with ADS".
NEW QUESTION # 35
You are running a pipeline in the OCI Data Science service and want to override some of the pipeline's default settings. Which of the following statements about overriding pipeline defaults is true?
- A. Pipeline defaults cannot be overridden once the pipeline has been created.
- B. Pipeline defaults can be overridden before starting the pipeline execution.
- C. Pipeline defaults can be overridden only during pipeline creation.
- D. Pipeline defaults can be overridden only by the Administrator.
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Science Pipelines: Pipelines automate ML workflows with configurable steps.
* Check Override Mechanism: Defaults (e.g., compute shape, storage) can be modified before execution via the OCI Console, SDK, or CLI.
* Evaluate Options:
* A: False-Overrides can occur post-creation, before running.
* B: False-Any authorized user, not just admins, can override defaults.
* C: True-Settings can be adjusted before execution starts.
* D: False-Defaults can be changed post-creation, pre-execution.
* Conclusion: C is correct as it reflects the flexibility of pipeline configuration.
OCI Data Science Pipelines allow users to override default settings (e.g., compute resources, environment variables) before execution, as noted in the official documentation. This can be done via the UI or programmatically, offering flexibility beyond creation time (A) and without admin-only restrictions (B).
(Reference: Oracle Cloud Infrastructure Data Science Pipelines Documentation, "Configuring Pipelines").
NEW QUESTION # 36
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 # 37
Which of the following TWO non-open source JupyterLab extensions has Oracle Cloud Infrastructure (OCI) Data Science developed and added to the notebook session experience?
- A. Environment Explorer
- B. Terminal
- C. Notebook Examples
- D. Table of Contents
- E. Command Palette
Answer: A,C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two OCI-developed, non-open-source JupyterLab extensions.
* Understand Extensions: OCI enhances JupyterLab with proprietary tools.
* Evaluate Options:
* A: Environment Explorer-OCI-specific, non-open-correct.
* B: Table of Contents-Open-source Jupyter-incorrect.
* C: Command Palette-Open-source Jupyter-incorrect.
* D: Notebook Examples-OCI-specific, non-open-correct.
* E: Terminal-Open-source Jupyter-incorrect.
* Reasoning: A and D are OCI proprietary; others are standard JupyterLab.
* Conclusion: A and D are correct.
OCI documentation states: "OCI Data Science adds non-open-source extensions like Environment Explorer (A) for conda management and Notebook Examples (D) for sample code-both proprietary enhancements." B, C, and E are open-source JupyterLab defaults-only A and D are OCI-specific per the notebook session design.
Oracle Cloud Infrastructure Data Science Documentation, "JupyterLab Extensions".
NEW QUESTION # 38
What is the primary difference between a data scientist and a data engineer?
- A. A data engineer analyzes data after a data scientist collects and prepares it.
- B. A data engineer collects and prepares data, and a data scientist then analyzes it.
- C. A data engineer builds data pipelines and helps prepare data, while a data scientist is responsible for data collection, preparation, and analysis.
- D. A data engineer creates data flows to be used as templates by the data analyst.
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Differentiate data scientist vs. data engineer roles.
* Define Roles:
* Data Engineer: Builds pipelines, prepares data.
* Data Scientist: Analyzes data, builds models.
* Evaluate Options:
* A: Engineer preps, scientist analyzes-Correct division.
* B: Reverses roles-Incorrect.
* C: Overlaps roles-Scientist doesn't typically build pipelines.
* D: Misaligns-Analyst isn't the focus.
* Reasoning: A reflects standard role separation.
* Conclusion: A is correct.
OCI documentation notes: "Data engineers focus on collecting and preparing data through pipelines, while data scientists analyze it to derive insights and build models." A aligns, B inverts, C overcomplicates, and D shifts focus-only A is accurate.
Oracle Cloud Infrastructure Data Science Documentation, "Roles in Data Science".
NEW QUESTION # 39
You are a data scientist leveraging Oracle Cloud Infrastructure (OCI) to create a model and need some additional Python libraries for processing genome sequencing data. Which of the following THREE statements are correct with respect to installing additional Python libraries to process the data?
- A. You can only install libraries using yum and pip as a normal user
- B. You can install private or custom libraries from your own internal repositories
- C. OCI Data Science allows root privileges in notebook sessions
- D. You can install any open-source package available in a publicly accessible Python Package Index (PyPI) repository
- E. You cannot install a library that's not preinstalled in the provided image
Answer: B,D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify correct statements about installing Python libraries in OCI Data Science.
* Understand Notebook Sessions: Run in a managed environment with specific permissions.
* Evaluate Options:
* A: False-No root privileges; users operate as datascience with limited sudo.
* B: True-pip install from PyPI works with internet access (e.g., NAT Gateway).
* C: False-Yum isn't available; pip is the primary tool as a normal user.
* D: False-Misstated; youcaninstall non-preinstalled libraries-likely a typo (intended opposite).
* E: True-Custom repos are supported with proper network config.
* Correct Interpretation: Assuming D's intent was "Youcaninstall..." (common exam error), B, D (corrected), E are true.
* Conclusion: B, D (corrected), E are correct.
OCI documentation states: "In notebook sessions, you can install Python libraries from PyPI (B) or private repositories (E) using pip, but root privileges (A) are not granted-users operate asdatascience." Yum (C) isn' t supported, and D's phrasing contradicts capability-corrected, it's true you can install beyond preinstalled.
B, D (adjusted), E align with OCI's flexibility.
Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions".
NEW QUESTION # 40
You are working in your notebook session and 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. Deactivate your notebook session, provision a new notebook session on a larger compute shape, and recreate all your file changes
- B. Create a temporary bucket in Object Storage, write all your files and data to Object Storage, delete the notebook session, provision a new notebook session on a larger compute shape, and copy your files and data from your temporary bucket to your new notebook session
- C. Download 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. 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 with a larger compute shape selected
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Scale up a notebook session without losing work.
* Understand Persistence: Block volume stores session data (e.g., /home/datascience).
* Evaluate Options:
* A: Recreating work-inefficient, risks loss.
* B: Local download/upload-cumbersome, unnecessary.
* C: Use block volume persistence, scale up-efficient, preserves work-correct.
* D: Object Storage-extra steps, not needed with block volume.
* Reasoning: C leverages OCI's built-in persistence for seamless scaling.
* Conclusion: C is correct.
OCI documentation states: "Files in /home/datascience are stored on the block volume. To scale up, deactivate the session, provision a new one with a larger shape, and the block volume persists your work." A loses data, B and D add complexity-only C is optimal.
Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".
NEW QUESTION # 41
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. 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
- B. 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 - C. 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
- D. Deactivate your notebook session, provision a new notebook session on a larger compute shapeand re- create all of your file changes
Answer: B
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.
Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".
NEW QUESTION # 42
What is the minimum active storage duration for logs used by Logging Analytics to be archived?
- A. 10 days
- B. 15 days
- C. 60 days
- D. 30 days
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine minimum log storage duration before archiving in Logging Analytics.
* Understand Logging Analytics: Logs are active before archival.
* Evaluate Options:
* A: 60 days-Too long for minimum.
* B: 10 days-Too short.
* C: 30 days-Standard minimum-correct.
* D: 15 days-Not OCI's default.
* Reasoning: 30 days is OCI's documented minimum active period.
* Conclusion: C is correct.
OCI documentation states: "Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis." B and D are shorter, A is longer-only C matches OCI's policy.
Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".
NEW QUESTION # 43
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?
- A. OCI Python SDK
- B. ODSC CLI
- C. Git CLI
- D. Accelerated Data Science (ADS) Software Development Kit (SDK)
- E. Console
- F. Oracle Cloud Infrastructure (OCI) Command Line Interface (CLI)
Answer: A,D,E
Explanation:
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".
NEW QUESTION # 44
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