Valid 1z0-1110-25 Practice Test Dumps with 100% Passing Guarantee [Jul-2025]
1z0-1110-25 PDF Dumps Are Helpful To produce Your Dreams Correct QA's
Oracle 1z0-1110-25 Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
| Topic 4 |
|
| Topic 5 |
|
NEW QUESTION # 96
Which of the following analytical and statistical techniques do data scientists commonly use?
- A. Clustering
- B. All of the above
- C. Classification
- D. Regression
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify common data science techniques.
* Define Techniques:
* Classification: Predicts categories (e.g., spam vs. not).
* Regression: Predicts continuous values (e.g., sales).
* Clustering: Groups data (e.g., customer segments).
* Evaluate Options:
* A, B, C: All are standard ML/statistical methods.
* D: Encompasses all-correct as they're widely used.
* Reasoning: These are foundational in data science workflows.
* Conclusion: D is correct.
OCI documentation lists "classification, regression, and clustering as core techniques in data science, supported by tools like ADS SDK and AutoML." All (D) are common per OCI's ML framework, not just subsets (A, B, C).
Oracle Cloud Infrastructure Data Science Documentation, "Analytical Techniques".
NEW QUESTION # 97
What is the minimum active storage duration for logs used by Logging Analytics to be archived?
- A. 15 days
- B. 60 days
- C. 10 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 # 98
How can you convert a fixed load balancer to a flexible load balancer?
- A. Use Update Shape workflows
- B. Delete the fixed load balancer and create a new one
- C. There is no way to convert the load balancer
- D. Using the Edit Listener option
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Convert fixed to flexible load balancer in OCI.
* Understand Load Balancers: Fixed (e.g., 10 Mbps) vs. flexible (dynamic shapes).
* Evaluate Options:
* A: False-Conversion possible via recreation.
* B: Update Shape-For flexible only, not conversion.
* C: Delete and recreate-Standard method-correct.
* D: Edit Listener-Configures rules, not type.
* Reasoning: OCI requires new creation for type change.
* Conclusion: C is correct.
OCI documentation states: "To change from a fixed to a flexible load balancer, delete the existing fixed load balancer and create a new flexible one (C)-direct conversion isn't supported." A is too absolute, B and D don't apply-only C matches OCI's process.
Oracle Cloud Infrastructure Load Balancing Documentation, "Changing Load Balancer Type".
NEW QUESTION # 99
You are a data scientist with a set of text and image files that need annotation, and you want to use Oracle Cloud Infrastructure (OCI) Data Labeling. Which of the following THREE annotation classes are supported by the tool?
- A. Object detection
- B. Key-point and landmark
- C. Named entity extraction
- D. Polygonal segmentation
- E. Semantic segmentation
- F. Classification (single/multi-label)
Answer: A,E,F
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify supported annotation classes in OCI Data Labeling.
* Understand Tool: Supports image/text annotations for ML.
* Evaluate Options:
* A: Object detection-Yes (bounding boxes).
* B: Named entity-Text-specific, not primary for images.
* C: Classification-Yes (labels for images/text).
* D: Key-point-Not listed in OCI docs.
* E: Polygonal-Not explicitly supported.
* F: Semantic segmentation-Yes (pixel-level).
* Reasoning: A, C, F match OCI's image/text focus.
* Conclusion: A, C, F are correct.
OCI Data Labeling supports "object detection (A), classification (C), and semantic segmentation (F) for images and text," per documentation. B is text-specific, D and E aren't highlighted-only A, C, F are core classes.
Oracle Cloud Infrastructure Data Labeling Documentation, "Annotation Types".
NEW QUESTION # 100
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. Table of Contents
- B. Terminal
- C. Environment Explorer
- D. Command Palette
- E. Notebook Examples
Answer: C,E
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 # 101
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor data. What are you trying to determine? How does the required False Alarm Probability setting affect an anomaly detection model?
- A. It is used to disable the reporting of false alarms
- B. It determines how many false alarms occur before an error message is generated
- C. It adds a score to each signal indicating the probability that it's a false alarm
- D. It changes the sensitivity of the model to detecting anomalies
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Understand FAP's effect in OCI Anomaly Detection.
* Evaluate Options:
* A: Disable reporting-Incorrect; FAP sets threshold.
* B: Sensitivity-Correct; lower FAP reduces false positives.
* C: Error message-Incorrect; not a count mechanism.
* D: Score per signal-Incorrect; FAP is a global setting.
* Reasoning: FAP adjusts detection threshold-key to sensitivity.
* Conclusion: B is correct.
OCI documentation states: "False Alarm Probability (FAP) controls the model's sensitivity-lower values reduce false positives, higher values increase detection." B aligns-others misrepresent FAP's role.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Configuration".
NEW QUESTION # 102
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 Dependence Explanations
- B. Feature Permutation Importance 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 # 103
Which components are a part of the OCI Identity and Access Management service?
- A. Compute instances
- B. Regional subnets
- C. VCN
- D. Policies
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify IAM components in OCI.
* Understand IAM: Manages access via policies, groups, etc.
* Evaluate Options:
* A: Policies-Core IAM component-correct.
* B: Subnets-Networking, not IAM.
* C: Instances-Compute, not IAM.
* D: VCN-Networking, not IAM.
* Reasoning: Only A is an IAM element-others are resources.
* Conclusion: A is correct.
OCI documentation states: "IAM includes components like policies (A), groups, and compartments to control resource access." B, C, and D are infrastructure, not IAM-only A fits per OCI's IAM framework.
Oracle Cloud Infrastructure IAM Documentation, "IAM Components".
NEW QUESTION # 104
Which function's objective is to represent the difference between the predictive value and the target value?
- A. Update function
- B. Optimizer function
- C. Cost function
- D. Fit function
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the function that measures the difference between predicted and actual values in machine learning.
* Understand ML Functions:
* Optimizer function: Adjusts model parameters to minimize error (e.g., gradient descent)-it uses the cost, not defines it.
* Fit function: Trains the model by fitting it to data-process-oriented, not a measure.
* Update function: Typically updates weights during training-not a standard term for error measurement.
* Cost function: Quantifies prediction error (e.g., MSE, cross-entropy)-directly represents the difference.
* Evaluate Options:
* A: Optimizer minimizes the cost, not the cost itself-incorrect.
* B: Fit executes training, not error definition-incorrect.
* C: Update is vague and not a standard ML term for this-incorrect.
* D: Cost function (e.g., loss) measures prediction vs. target-correct.
* Reasoning: The cost function (or loss function) is the mathematical representation of error, guiding optimization.
* Conclusion: D is the correct answer.
In OCI Data Science, the documentation explains: "The cost function (or loss function) measures the difference between the model's predicted values and the actual target values, such as mean squared error for regression or cross-entropy for classification." Optimizers (A) use this to adjust weights, fit (B) is a training step, and update (C) isn't a defined function here-only the cost function (D) fits the description. This aligns with standard ML terminology and OCI's AutoML processes.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Concepts - Cost Functions".
NEW QUESTION # 105
Which of these is a unique feature of the published conda environment?
- A. Provides availability on network session reactivation
- B. Provides a comprehensive environment to solve business use cases
- C. Allows you to save the conda environment in a block volume
- D. Allows you to save the conda environment to an Object Storage Bucket
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand Published Conda Environments: In OCI Data Science, these are custom conda environments shared across users via Object Storage.
* Evaluate Options:
* A: Vague-All conda environments can address use cases; not unique to "published."
* B: Incorrect-Availability on reactivation applies to session persistence, not publishing.
* C: Correct-Publishing saves the environment to Object Storage for sharing/reuse.
* D: Incorrect-Block volumes store session data, not published environments.
* Reasoning: The unique aspect of "published" environments is their storage in Object Storage (via odsc conda publish), enabling team access.
* Conclusion: C is the distinctive feature.
The OCI Data Science documentation highlights that "published conda environments are saved to an OCI Object Storage Bucket, allowing them to be shared across notebook sessions and users." This distinguishes C from A (generic), B (session-related), and D (block volume is for session state, not publishing). Publishing to Object Storage is the defining trait per Oracle's design.
Oracle Cloud Infrastructure Data Science Documentation, "Managing Conda Environments - Publishing" section.
NEW QUESTION # 106
Which statement about logs for Oracle Cloud Infrastructure Jobs is true?
- A. Integrating data science jobs resources with logging is mandatory
- B. Each job run sends outputs to a single log for that job
- C. Logs are automatically deleted when the job and job run is deleted
- D. All stdout and stderr are automatically stored when automatic log creation is enabled
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a true statement about OCI Jobs logging.
* Understand Logging: Jobs can log stdout/stderr to OCI Logging service.
* Evaluate Options:
* A: False-Each run has its own log, not a single job log.
* B: False-Logging is optional, not mandatory.
* C: True-When enabled, stdout/stderr are auto-captured.
* D: False-Logs persist unless explicitly deleted.
* Reasoning: C matches OCI's automatic logging feature.
* Conclusion: C is correct.
OCI documentation states: "When automatic log creation is enabled for Data Science Jobs, all stdout and stderr outputs are captured and stored in the OCI Logging service." A is incorrect (per-run logs), B is optional, and D contradicts log retention-only C is accurate.
Oracle Cloud Infrastructure Data Science Documentation, "Jobs Logging".
NEW QUESTION # 107
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?
- A. OCI Speech
- B. OCI Language
- C. OCI Vision
- D. Oracle Digital Assistant
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.
Oracle Cloud Infrastructure Language Documentation, "Language Detection".
NEW QUESTION # 108
You are a data scientist; you use the Oracle Cloud Infrastructure (OCI) Language service to train custom models. Which types of custom models can be trained?
- A. Object detection, Text classification
- B. Sentiment Analysis, Named Entity Recognition (NER)
- C. Text classification, Named Entity Recognition (NER)
- D. Image classification, Named Entity Recognition (NER)
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify custom model types for OCI Language.
* Understand OCI Language: Focuses on text analysis.
* Evaluate Options:
* A: Image classification-Not text-based, incorrect.
* B: Text classification, NER-Both text tasks-correct.
* C: Sentiment, NER-Sentiment is pretrained, not custom.
* D: Object detection-Image-based, incorrect.
* Reasoning: B aligns with OCI Language's text custom models.
* Conclusion: B is correct.
OCI Language documentation states: "Custom models can be trained for text classification and Named Entity Recognition (NER) using your data." Image tasks (A, D) are for Vision, and sentiment (C) is pretrained- only B fits OCI Language's scope.
Oracle Cloud Infrastructure Language Documentation, "Custom Model Training".
NEW QUESTION # 109
You want to make your model more parsimonious to reduce the cost of collecting and processing data. 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. corr()
- B. cramersv_plot()
- C. correlation_ratio_plot()
- D. pearson_plot()
Answer: C
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.
Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization Methods".
NEW QUESTION # 110
You have created a conda environment in your notebook session. This is the first time you are working with published conda environments. You have also created an Object Storage bucket with permission to manage the bucket. Which TWO commands are required to publish the conda environment?
- A. odsc conda init --bucket_namespace <NAMESPACE> --bucket_name <BUCKET>
- B. odsc conda list --override
- C. odsc conda publish --slug <SLUG>
- D. conda activate /home/datascience/conda/<SLUG>
- E. odsc conda create --file manifest.yaml
Answer: A,C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Publish a conda env to Object Storage.
* Process: Initialize bucket config, then publish env.
* Evaluate Options:
* A: Publishes env with slug-correct final step.
* B: Lists envs-unrelated to publishing.
* C: Sets bucket details-required setup-correct.
* D: Creates env-not publishing.
* E: Activates env-not for sharing.
* Reasoning: C sets up, A executes-standard workflow.
* Conclusion: A and C are correct.
OCI documentation states: "To publish a conda environment, first run odsc conda init (C) with bucket namespace and name, then odsc conda publish (A) with a slug to upload to Object Storage." B, D, and E serve other purposes-only A and C are required per OCI's process.
Oracle Cloud Infrastructure Data Science CLI Reference, "Publishing Conda Environments".
NEW QUESTION # 111
Which of these protects customer data at rest and in transit in a way that allows customers to meet their security and compliance requirements for cryptographic algorithms and key management?
- A. Data encryption
- B. Security controls
- C. Customer isolation
- D. Identity Federation
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify protection for data at rest/transit with customer control.
* Evaluate Options:
* A: Controls-Broad, not specific to encryption.
* B: Isolation-Separates tenants, not crypto-focused.
* C: Encryption-Secures data, allows key management-correct.
* D: Federation-Auth sharing, not data protection.
* Reasoning: C provides crypto control (e.g., Vault keys).
* Conclusion: C is correct.
OCI documentation states: "Data encryption (C) protects data at rest and in transit, with customer-managed keys in OCI Vault meeting compliance needs." A and B are broader, D is unrelated-only C fits per OCI's security model.
Oracle Cloud Infrastructure Security Documentation, "Data Encryption".
NEW QUESTION # 112
Which CLI command allows the customized conda environment to be shared with co-workers?
- A. odsc conda modify
- B. odsc conda publish
- C. odsc conda install
- D. odsc conda clone
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Share a custom conda environment in OCI Data Science.
* Understand Commands: OCI provides odsc CLI for environment management.
* Evaluate Options:
* A: clone duplicates an environment locally-not for sharing.
* B: publish uploads the environment to Object Storage for team access-correct.
* C: modify doesn't exist as a standard command.
* D: install sets up an environment locally-not for sharing.
* Reasoning: Sharing requires publishing to a shared location (Object Storage), which publish achieves.
* Conclusion: B is the correct command.
The OCI Data Science CLI documentation states: "Use odsc conda publish to package and upload a custom conda environment to an Object Storage Bucket, making it accessible to other users." clone (A) is for local duplication, modify (C) isn't valid, and install (D) is for local setup-not sharing. B is the designated sharing mechanism.
Oracle Cloud Infrastructure Data Science CLI Reference, "odsc conda publish".
NEW QUESTION # 113
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 Catalog
- B. Model Depository
- C. OCI Vault
- D. Data Science Artifactory
Answer: A
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.
Oracle Cloud Infrastructure Data Science Documentation, "ADS Model Deployment".
NEW QUESTION # 114
You are using a custom application with third-party APIs to manage application and data hosted in an Oracle Cloud Infrastructure (OCI) tenancy. Although your third-party APIs don't support OCI's signature-based authentication, you want them to communicate with OCI resources. Which authentication option must you use to ensure this?
- A. OCI username and password
- B. API Signing Key
- C. Auth Token
- D. SSH Key Pair with 2048-bit algorithm
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an auth method for third-party APIs lacking OCI signature support.
* Understand OCI Auth: Typically uses API keys, but alternatives exist for non-standard APIs.
* Evaluate Options:
* A: Username/password-Not API-friendly, insecure.
* B: API Signing Key-Requires signature-based auth, unsupported here.
* C: SSH Key-For instance access, not APIs.
* D: Auth Token-Simple token for API calls-correct.
* Reasoning: Auth Token provides a bearer token for APIs without signature complexity.
* Conclusion: D is correct.
OCI documentation states: "For third-party APIs not supporting signature-based authentication, use an Auth Token (D), a secure, revocable token for accessing OCI resources via REST APIs." A, B, and C don't fit non- signature scenarios-only D ensures compatibility per OCI's IAM options.
Oracle Cloud Infrastructure IAM Documentation, "Auth Tokens for API Access".
NEW QUESTION # 115
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor data. How does the required False Alarm Probability setting affect an anomaly detection model?
- A. It is used to disable the reporting of false alarms
- B. It determines how many false alarms occur before an error message is generated
- C. It adds a score to each signal indicating the probability that it's a false alarm
- D. It changes the sensitivity of the model to detecting anomalies
Answer: D
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.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Settings".
NEW QUESTION # 116
You have an embarrassingly parallel or distributed batch job with a large amount of data running using Data Science Jobs. What would be the best approach to run the workload?
- A. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel
- B. Create a new job for every job run that you have to run in parallel, because the Data Science Job service can have only one job per job
- C. Create the job in Data Science Jobs and start a job run. When it is done, start a new job run until you achieve the number of runs required
- D. Create a job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize an embarrassingly parallel job in OCI Data Science Jobs.
* Define Embarrassingly Parallel: Tasks are independent, ideal for simultaneous runs.
* Evaluate Options:
* A: Multiple simultaneous runs-Leverages parallelism-correct.
* B: One job per run-Misstates capability; unnecessary complexity.
* C: Sequential runs-Inefficient, ignores parallelism.
* D: False-Jobs support parallelism.
* Reasoning: A maximizes efficiency for parallel tasks.
* Conclusion: A is correct.
OCI documentation states: "For embarrassingly parallel workloads, create a single Job and launch multiple simultaneous Job Runs to process data in parallel." B misinterprets limits, C wastes time, and D denies capability-only A fits OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Runs".
NEW QUESTION # 117
You are attempting to save a model from a notebook session to the model catalog by using ADS SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which TWO should you look for to ensure permissions are set up correctly?
- A. The policy for the dynamic group grants manage permissions for the model catalog in this compartment
- B. The model artifact is saved to the block volume of the notebook session
- C. The policy for your user group grants manage permissions for the model catalog in this compartment
- D. The dynamic groups matching rule exists for notebook sessions in the compartment
- E. The networking configuration allows access to the Oracle Cloud Infrastructure services through a service gateway
Answer: A,D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Troubleshoot a 404 authentication error when saving a model using ADS SDK with resource principal.
* Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies-no user credentials needed.
* Analyze 404 Error: Indicates an authorization failure-likely missing permissions or misconfigured resource principal.
* Evaluate Options:
* A: True-Dynamic group must include notebook sessions (e.g., resource.type =
'datasciencenotebooksession') to authenticate.
* B: False-Block volume stores artifacts locally, but saving to the catalog is a permission issue, not storage.
* C: True-Policy must grant manage data-science-models to the dynamic group for catalog access.
* D: False-Service gateway ensures network access, but 404 is auth-related, not connectivity.
* E: False-Resource principal uses dynamic group policies, not user group policies.
* Reasoning: A (group inclusion) and C (policy permission) are critical for resource principal auth- others are tangential.
* Conclusion: A and C are correct.
OCI documentation states: "To use resource principal with ADS SDK for model catalog operations, ensure (1) a dynamic group includes the notebook session with a matching rule (e.g., all {resource.type =
'datasciencenotebooksession'}) and (2) a policy grants the dynamic group manage data-science-models permissions in the compartment." B is unrelated (storage location), D is network-focused, and E applies to user auth-not resource principal. A 404 error flags missing auth, fixed by A and C.
Oracle Cloud Infrastructure Data Science Documentation, "Using Resource Principals with ADS SDK".
NEW QUESTION # 118
......
Cover 1z0-1110-25 Exam Questions Make Sure You 100% Pass: https://guidetorrent.dumpstorrent.com/1z0-1110-25-exam-prep.html