[Aug-2026] Pass 1Z0-1110-26 Exam in First Attempt Updated 1Z0-1110-26 Exam Questions
Oracle Cloud Dumps 1Z0-1110-26 Exam for Full Questions - Exam Study Guide
NEW QUESTION # 30
Which model has an open-source, open model format that allows you to run machine learning models on different platforms?
- A. ONNX
- B. PyTorch
- C. TensorFlow
- D. PySpark
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify an open model format for cross-platform ML model execution.
Evaluate Options:
A . PySpark: A big data framework, not a model format.
B . PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.
C . TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.
D . ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.
Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.
Conclusion: D is the correct choice.
ONNX (D) is “an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow,” per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats—only ONNX ensures cross-platform compatibility.
1: Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".
NEW QUESTION # 31
For your next data science project, you need access to public geospatial images. Which Oracle Cloud service provides free access to those images?
- A. Oracle Cloud Infrastructure (OCI) Data Science
- B. Oracle Big Data Service
- C. Oracle Open Data
- D. Oracle Analytics Cloud
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find the OCI service for free geospatial images.
Evaluate Options:
A: Big Data Service—Spark processing, not datasets.
B: Analytics Cloud—Visualization, not data source.
C: Data Science—ML platform, not dataset provider.
D: Open Data—Free public datasets, including geospatial—correct.
Reasoning: Open Data is OCI’s public dataset hub.
Conclusion: D is correct.
OCI documentation states: “Oracle Open Data provides free access to curated datasets, including geospatial images, for public use.” A, B, and C serve other purposes—only D delivers free geospatial data.
1: Oracle Cloud Infrastructure Open Data Documentation, "Dataset Offerings".
NEW QUESTION # 32
You have received machine learning model training code, without clear information about the optimal shape to run the training. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?
- A. Start with a random compute shape and monitor the utilization metrics and time required to finish the model training. Perform model training optimizations and performance tests in advance to identify the right compute shape before running the model training as a job
- B. Start with a smaller shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and rerun the job. Repeat the process until the processing time does not improve
- C. Start with a smaller shape and monitor the Job Run metrics and time required to complete the model training. If the compute shape is not fully utilized, tune the model parameters, and rerun the job. Repeat the process until the shape resources are fully utilized
- D. Start with the strongest compute shape Jobs support and monitor the Job Run metrics and time required to complete the model training. Tune the model so that it utilizes as much compute resources as possible, even at an increased cost
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Optimize compute shape for cost and time.
Evaluate Options:
A: Tuning params—Focuses on model, not shape.
B: Strongest shape—Costly, unbalanced.
C: Scale up when utilized—Balances cost/time—correct.
D: Random start—Unsystematic.
Reasoning: C iteratively optimizes based on utilization.
Conclusion: C is correct.
OCI documentation advises: “Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes—optimizes cost and speed.” A misfocuses, B overspends, D lacks method—only C aligns.
1: Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".
NEW QUESTION # 33
You have created a model and want to use Accelerated Data Science (ADS) SDK to deploy the model. Where are the artifacts to deploy this model with ADS?
- A. Model Depository
- B. OCI Vault
- C. Model Catalog
- D. Data Science Artifactory
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate artifacts for ADS model deployment.
Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI.
Evaluate Options:
A: Vault—Stores secrets, not models.
B: Depository—Not an OCI term.
C: Model Catalog—Stores models/artifacts for deployment—correct.
D: Artifactory—Not an OCI service.
Reasoning: Model Catalog is OCI’s model repository for ADS.
Conclusion: C is correct.
OCI documentation states: “ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored.” Vault (A) is for secrets, B and D aren’t real—only C supports ADS deployment.
1: Oracle Cloud Infrastructure Data Science Documentation, "ADS Model Deployment".
NEW QUESTION # 34
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image dat a. Which of the following THREE are possible ways to annotate an image in Data Labeling?
- A. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image
- B. Adding multiple labels to an image
- C. Adding a single label to an image
- D. Adding labels to an image using object detection, by drawing bounding boxes to an image
- E. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
Answer: B,C,D
NEW QUESTION # 35
What happens when a notebook session is deactivated?
- A. The underlying compute instance stops
- B. The data on boot volume is preserved
- C. Compute cost increases due to frequent deactivation
- D. The block volume attached to the notebook is permanently deleted
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Notebook Sessions: These are OCI compute instances running JupyterLab.
Deactivation Impact: Deactivating stops the session to save costs.
Evaluate Options:
A: False—Costs decrease as compute stops.
B: False—Boot volume data isn’t preserved; block volume data is.
C: True—The compute instance shuts down, halting billing.
D: False—Block volume persists unless explicitly deleted.
Reasoning: Deactivation stops the instance (C), preserving block volume data separately.
Conclusion: C is correct.
The OCI documentation states: “When a notebook session is deactivated, the underlying compute instance stops, and billing for compute resources ceases. Data on the attached block volume is preserved, but the boot volume is not.” A is backwards, B misattributes preservation, and D overstates deletion—only C aligns with the process.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Lifecycle".
NEW QUESTION # 36
After you have created and opened a notebook session, you want to use the Accelerated Data Science (ADS) SDK to access your data and get started with exploratory data analysis. From which TWO places can you access the ADS SDK?
- A. Oracle Machine Learning
- B. Oracle Big Data Service
- C. Oracle Autonomous Data Warehouse
- D. Python Package Index (PyPI)
- E. Conda environment in OCI Data Science
Answer: D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate sources for ADS SDK in OCI.
Understand ADS SDK: A Python library for Data Science tasks (e.g., EDA).
Evaluate Options:
A: Big Data Service—Spark-focused, not ADS source.
B: Machine Learning—Separate service, not ADS-related.
C: Conda in OCI Data Science—Preinstalled ADS in notebook sessions.
D: PyPI—Public source to install ADS (pip install oracle-ads).
E: ADW—Database, not an SDK source.
Reasoning: C (preinstalled) and D (installable) are practical access points.
Conclusion: C and D are correct.
OCI documentation states: “The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle-ads.” Big Data (A), Machine Learning (B), and ADW (E) don’t host ADS—only C and D apply.
1: Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".
NEW QUESTION # 37
Which of these options allow the sharing and loading back of ML models into a notebook session?
- A. Model catalog
- B. Model taxonomy
- C. Model deployment
- D. Model provenance
Answer: A
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".
NEW QUESTION # 38
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. Use environmental variables
- C. Configure a compute shape
- D. Configure a block volume
Answer: B
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".
NEW QUESTION # 39
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.
- A. Key rotation reduces risk if a key is ever compromised
- B. Key rotation allows you to encrypt no more than five keys at a time
- C. Periodically rotating keys makes it easier to reuse keys
- D. Key rotation improves encryption efficiency
- E. Periodically rotating keys limits the amount of data encrypted by one key version
Answer: A,E
Explanation:
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.
1: Oracle Cloud Infrastructure Vault Documentation, "Key Rotation Benefits".
NEW QUESTION # 40
You want to write a Python script to create a collection of different projects for your data science team. Which Oracle Cloud Infrastructure (OCI) Data Science interface would you use?
- A. Command Line Interface (CLI)
- B. The OCI Software Development Kit (SDK)
- C. Mobile App
- D. OCI Console
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Choose an interface for a Python script to manage projects.
Evaluate Options:
A: OCI SDK—Python-based, scriptable—correct.
B: Console—GUI, not scriptable.
C: CLI—Command-based, not Python-native.
D: Mobile App—Not for scripting.
Reasoning: A enables programmatic project creation.
Conclusion: A is correct.
OCI documentation states: “Use the OCI Python SDK (A) to programmatically manage Data Science resources, like creating projects, via Python scripts.” B, C, and D don’t support Python scripting—only A fits.
1: Oracle Cloud Infrastructure SDK Documentation, "Data Science API".
NEW QUESTION # 41
Triggering a PagerDuty notification as part of Monitoring is an example of what in the OCI Console?
- A. Event
- B. Function
- C. Action
- D. Rule
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Classify a PagerDuty notification in OCI Monitoring.
Understand OCI Monitoring: Involves events, rules, and actions for notifications.
Evaluate Options:
A: Action—executes a response (e.g., notify PagerDuty) when triggered—correct.
B: Rule—defines conditions for triggering actions—precedes the action.
C: Function—serverless code, not directly tied to notifications.
D: Event—state change triggering a rule, not the notification itself.
Reasoning: The notification is the action taken after an event/rule—fits A.
Conclusion: A is correct.
OCI documentation states: “Actions in the Monitoring service execute responses, such as sending notifications to PagerDuty, when a rule’s condition is met based on an event.” Rules (B) set conditions, Functions (C) are unrelated, and Events (D) are triggers—only Action (A) describes the notification step.
1: Oracle Cloud Infrastructure Monitoring Documentation, "Actions Overview".
NEW QUESTION # 42
What is the primary difference between a data scientist and a data engineer?
- A. A data engineer builds data pipelines and helps prepare data, while a data scientist is responsible for data collection, preparation, and analysis.
- B. A data engineer collects and prepares data, and a data scientist then analyzes it.
- C. A data engineer creates data flows to be used as templates by the data analyst.
- D. A data engineer analyzes data after a data scientist collects and prepares it.
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Roles in Data Science".
NEW QUESTION # 43
What is the minimum active storage duration for logs used by Logging Analytics to be archived?
- A. 30 days
- B. 10 days
- C. 15 days
- D. 60 days
Answer: A
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.
1: Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".
NEW QUESTION # 44
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. Updating the local repo to match the content from a remote repository
- B. Converting an existing local project folder to a Git repository
- C. Moving the changes into staging area for the next commit
- D. Pushing changes to a remote repository
- E. Making a commit that is taking a snapshot of the local repository for the next push
Answer: A,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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".
NEW QUESTION # 45
You are given the task of writing a program that sorts document images by language. Which Oracle service would you use?
- A. OCI Vision
- B. OCI Language
- C. Oracle Digital Assistant
- D. OCI Speech
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the Oracle service to sort document images by language.
Task Breakdown: Requires extracting text from images (OCR) and detecting language—two potential services involved.
Evaluate Options:
A . Oracle Digital Assistant: Builds chatbots—irrelevant to image or language processing.
B . OCI Language: Detects and classifies languages in text—ideal for sorting after text extraction.
C . OCI Speech: Transcribes audio to text—not applicable to images.
D . OCI Vision: Performs OCR to extract text from images—necessary but not sufficient for language sorting.
Reasoning: The task emphasizes “sorting by language.” OCI Vision extracts text, but OCI Language identifies the language (e.g., English, Spanish). Since the question asks for one service and focuses on sorting, OCI Language (B) is the best fit, assuming text extraction is a precursor step.
Conclusion: B is correct.
OCI Language “provides language detection and classification capabilities, enabling identification of languages in text extracted from documents,” per the documentation. OCI Vision handles OCR, but the sorting task aligns with OCI Language (B). Digital Assistant (A) and Speech (C) don’t apply, and while Vision (D) is a prerequisite, B is the primary service for language sorting as per OCI’s AI service design.
1: Oracle Cloud Infrastructure Language Documentation, "Language Detection Features".
NEW QUESTION # 46
As a data scientist, you create models for cancer prediction based on mammographic images. The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix. Which model would you prefer and why?
Model 1 has Test accuracy is 80% and recall is 70%
Model 2 has Test accuracy is 75% and recall is 85%
- A. Model 2, because recall has more impact on predictions in this use case
- B. Model 2, because recall is high
- C. Model 1, because recall has lesser impact on predictions in this use case
- D. Model 1, because the test accuracy is high
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Choose the better model for cancer prediction based on metrics.
Understand Metrics:
Accuracy: Overall correct predictions.
Recall: True positives / (True positives + False negatives)—crucial for cancer (minimizing misses).
Context: Cancer prediction prioritizes recall—false negatives (missed cancers) are critical.
Evaluate Models:
Model 1: 80% accuracy, 70% recall—Misses more cancers.
Model 2: 75% accuracy, 85% recall—Misses fewer cancers.
Evaluate Options:
A: High recall—True, but lacks context.
B: High accuracy—Misses recall’s importance.
C: Recall’s impact—Correct for cancer use case—best.
D: Lesser recall impact—Incorrect for this priority.
Reasoning: C emphasizes recall’s critical role—aligns with medical needs.
Conclusion: C is correct.
OCI documentation advises: “For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at 85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C).” A is partial, B overlooks context, D reverses priority—only C fits OCI’s ML evaluation guidance for this scenario.
1: Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".
NEW QUESTION # 47
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?
- A. cramersv_plot()
- B. correlation_ratio_plot()
- C. pearson_plot()
- D. corr()
Answer: B
Explanation:
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.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization Methods".
NEW QUESTION # 48
Which Oracle Cloud Infrastructure (OCI) Data Science policy is invalid?
- A. Allow dynamic-group DataScienceDynamicGroup to manage data-science-projects in compartment DataScience
- B. Allow group DataScienceGroup to use data-science-model-sessions in compartment DataScience
- C. Allow group DataScienceGroup to use virtual-network-family in compartment DataScience
- D. Allow dynamic-group DataScienceDynamicGroup to manage data-science-family in compartment DataScience
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Spot the invalid OCI IAM policy.
Understand Policies: Use verbs (e.g., use, manage) and resource types.
Evaluate Options:
A: Valid—virtual-network-family is a resource type.
B: Invalid—No data-science-model-sessions resource type; likely meant data-science-models.
C: Valid—data-science-projects is correct.
D: Valid—data-science-family aggregates Data Science resources.
Reasoning: B uses a nonexistent resource type—error in syntax.
Conclusion: B is incorrect.
OCI IAM documentation lists valid resource types: “data-science-models, data-science-projects, data-science-family—but not data-science-model-sessions (B).” A, C, D are correct—B is invalid due to this typo/misunderstanding.
1: Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".
NEW QUESTION # 49
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?
- A. OCI Vision
- B. OCI Language
- C. Oracle Digital Assistant
- D. OCI Speech
Answer: B
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.
1: Oracle Cloud Infrastructure Language Documentation, "Language Detection".
NEW QUESTION # 50
You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many sub-folders and classes. What is the best way to run this project as a Job?
- A. ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file automatically
- B. Rewrite your code so that it is a single executable Python or Bash/Shell script file
- C. ZIP the entire code project folder, upload it as a Job artifact on job creation, and set JOB_RUN_ENTRYPOINT to point to the main executable file
- D. ZIP the entire code project folder and upload it as a Job artifact. Jobs automatically identifies the main top-level where the code is run
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Run a complex Python project as an OCI Job.
Evaluate Options:
A: Auto-identification—False; entrypoint must be set.
B: Rewrite—Unnecessary, inefficient.
C: Auto-executable—False; needs explicit entrypoint.
D: ZIP with entrypoint—Correct, flexible approach.
Reasoning: D preserves structure, specifies execution.
Conclusion: D is correct.
OCI documentation states: “For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py).” Auto-detection (A, C) isn’t supported, and B discards structure—D is best.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".
NEW QUESTION # 51
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