[Feb-2026] Dumps Brief Outline Of The 1z0-1110-25 Exam - TestkingPDF [Q87-Q110]

[Feb-2026] Dumps Brief Outline Of The 1z0-1110-25 Exam - TestkingPDF

1z0-1110-25 Training & Certification Get Latest Oracle Cloud

NEW QUESTION # 87
You want to make your model more frugal 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 is appropriate to display the comparability between Continuous and Categorical features?

• A. corr()
• B. pearson_plot()
• C. cramersv_plot()
• D. correlation_ratio_plot()

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Visualize correlation between continuous and categorical features.
* Evaluate Options:
* A: Pearson-Continuous vs. continuous-incorrect.
* B: Cramer's V-Categorical vs. categorical-incorrect.
* C: Correlation ratio-Continuous vs. categorical-correct.
* D: General correlation-Not specific to mixed types.
* Reasoning: Correlation ratio handles mixed feature types for heatmaps.
* Conclusion: C is correct.
OCI documentation states: "correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps." Pearson (A) and Cramer's (B) are type- specific, corr() (D) is broad-only C fits per ADS capabilities.
Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".

NEW QUESTION # 88
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. Python Package Index (PyPI)
• D. Oracle Autonomous Data Warehouse
• E. Conda environment in OCI Data Science

Answer: C,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.
Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".

NEW QUESTION # 89
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. Data Science Artifactory
• D. Model Catalog

Answer: D

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 # 90
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which of the following TWO prevailing metrics would you use for evaluating a multiclass classification model?

• A. Explained variance score
• B. F1 Score
• C. Recall
• D. R-Squared
• E. Mean squared error

Answer: B,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select two metrics for multiclass classification in AutoML.
* Understand Multiclass Metrics: Focus on class-specific performance-classification, not regression.
* Evaluate Options:
* A. Recall: Measures true positives per class-key for multiclass-correct.
* B. Mean squared error: Regression metric-incorrect.
* C. F1 Score: Balances precision and recall-standard for multiclass-correct.
* D. R-Squared: Regression fit-incorrect.
* E. Explained variance: Regression metric-incorrect.
* Reasoning: A and C assess classification accuracy across multiple classes-fit AutoML's evaluation.
* Conclusion: A and C are correct.
OCI AutoML documentation states: "For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance." B, D, and E are regression- focused-only A and C are supported and relevant per OCI's AutoML metrics suite.
Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".

NEW QUESTION # 91
You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?

• A. Create a new job for every job run that you have to run in parallel, because the Data Science Jobs service can have only one job run per job
• B. Create the job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
• 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. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel workloads

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize embarrassingly parallel workload in OCI Jobs.
* Evaluate Options:
* A: Sequential runs-Inefficient for parallel tasks.
* B: Simultaneous runs-Maximizes parallelism-correct.
* C: False-Jobs support parallelism.
* D: One job per run-Misstates capability, wasteful.
* Reasoning: B leverages OCI's parallel run support.
* Conclusion: B is correct.
OCI documentation states: "For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently." A is slow, C is incorrect, and Dovercomplicates-B is the best approach.
Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".

NEW QUESTION # 92
You realize that your model deployment is about to reach its utilization limit. What would you do to avoid the issue before requests start to fail? Pick THREE.

• A. Update the deployment to use a larger virtual machine (more CPUs/memory)
• B. Reduce the load balancer bandwidth limit so that fewer requests come in
• C. Delete the deployment
• D. Update the deployment to use fewer instances
• E. Update the deployment to add more instances

Answer: A,B,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Prevent deployment failure due to high utilization.
* Evaluate Options:
* A: More instances-Scales capacity-correct.
* B: Delete-Stops service, not a solution.
* C: Fewer instances-Worsens utilization.
* D: Larger VM-Increases resource capacity-correct.
* E: Reduce bandwidth-Limits load-correct.
* Reasoning: A and D boost capacity, E controls demand-proactive fixes.
* Conclusion: A, D, E are correct.
OCI documentation advises: "To handle high utilization, increase instances (A), use a larger compute shape (D), or adjust load balancer bandwidth (E) to manage request volume." B stops service, C reduces capacity- only A, D, E prevent failure per OCI's scaling options.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".

NEW QUESTION # 93
Which is NOT a compliance document?

• A. Attestation
• B. Certificate
• C. Penetration test report
• D. Bridge letter

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a non-compliance document in OCI context.
* Understand Compliance Docs: Formal attestations of adherence (e.g., SOC, ISO).
* Evaluate Options:
* A: Certificate-Proof of compliance (e.g., ISO)-compliance doc.
* B: Pen test report-Security test result, not formal compliance-correct.
* C: Attestation-Statement of compliance-compliance doc.
* D: Bridge letter-Links audit periods-compliance doc.
* Reasoning: B is operational, not a compliance artifact.
* Conclusion: B is correct.
OCI documentation lists "compliance documents like certificates (A), attestations (C), and bridge letters (D) for standards like SOC or ISO; penetration test reports (B) are security assessments, not formal compliance docs." Only B stands apart per OCI's compliance terminology.
Oracle Cloud Infrastructure Compliance Documentation, "Compliance Artifacts".

NEW QUESTION # 94
Which type of file system does File Storage use?

• A. Paravirtualized
• B. NVMe SSD
• C. iSCSI
• D. NFSv3

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the file system type for OCI File Storage.
* Understand File Storage: Network-attached storage in OCI.
* Evaluate Options:
* A: NFSv3-Network File System, used by File Storage-correct.
* B: iSCSI-Block storage protocol, not File Storage.
* C: Paravirtualized-Virtualization mode, not file system.
* D: NVMe SSD-Hardware, not file system.
* Reasoning: NFSv3 is OCI File Storage's protocol.
* Conclusion: A is correct.
OCI documentation states: "File Storage uses NFSv3 (A) as its file system protocol, providing shared storage across instances." B, C, and D are unrelated-only A aligns with OCI's File Storage design.
Oracle Cloud Infrastructure File Storage Documentation, "File System Protocol".

NEW QUESTION # 95
What happens when a notebook session is deactivated?

• A. Compute cost increases due to frequent deactivation
• B. The underlying compute instance stops
• C. The block volume attached to the notebook is permanently deleted
• D. The data on boot volume is preserved

Answer: B

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.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Lifecycle".

NEW QUESTION # 96
Which technique can be used for feature engineering in the machine learning lifecycle?

• A. Gradient boosting
• B. K-means clustering
• C. Support Vector Machines (SVM)
• D. Principal Component Analysis (PCA)

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a feature engineering technique in ML.
* Understand Feature Engineering: Transforms raw data into model-ready features.
* Evaluate Options:
* A. PCA: Reduces dimensionality-feature engineering-correct.
* B. K-means: Clustering model-not feature engineering.
* C. SVM: Classification model-not feature engineering.
* D. Gradient boosting: Model training-not feature engineering.
* Reasoning: PCA creates new features via transformation-fits definition.
* Conclusion: A is correct.
OCI documentation states: "Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance." B, C, and D are modeling techniques-only A aligns with OCI's feature engineering stage.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".

NEW QUESTION # 97
Which stage in the machine learning life cycle helps in identifying the imbalance present in the data?

• A. Data Monitoring
• B. Data Access
• C. Data Exploration
• D. Data Modeling

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find the stage where data imbalance (e.g., skewed classes) is identified.
* Understand Stages:
* Data Modeling: Training models-assumes data is prepared.
* Data Monitoring: Post-deployment tracking-not for initial analysis.
* Data Exploration: Analyzing data properties (e.g., distributions)-key for imbalance.
* Data Access: Retrieving data-no analysis yet.
* Evaluate Options:
* A: Modeling uses data, doesn't detect imbalance-incorrect.
* B: Monitoring tracks performance, not initial data issues-incorrect.
* C: Exploration (e.g., via pandas) reveals imbalances-correct.
* D: Access is just retrieval-incorrect.
* Reasoning: Imbalance is assessed during exploration (e.g., class counts).
* Conclusion: C is correct.
OCI documentation notes: "Data Exploration involves analyzing the dataset to understand its characteristics, such as identifying class imbalances or missing values, using tools like ADS SDK or Jupyter notebooks." Modeling (A) and Monitoring (B) occur later, while Access (D) is pre-analysis-only Exploration (C) fits this role.
Oracle Cloud Infrastructure Data Science Documentation, "Data Exploration Stage".

NEW QUESTION # 98
You are a data scientist leveraging the Oracle Cloud Infrastructure (OCI) Language AI service for various types of text analyses. Which TWO capabilities can you utilize with this tool?

• A. Sentence diagramming
• B. Table extraction
• C. Sentiment analysis
• D. Topic classification
• E. Punctuation correction

Answer: C,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two OCI Language AI capabilities.
* Understand OCI Language: Focuses on text analysis tasks.
* Evaluate Options:
* A: Table extraction-Vision, not Language-incorrect.
* B: Punctuation correction-Not offered-incorrect.
* C: Sentence diagramming-Not supported-incorrect.
* D: Topic classification-Supported (custom/pretrained)-correct.
* E: Sentiment analysis-Supported (pretrained)-correct.
* Reasoning: D and E are core text analysis features of OCI Language.
* Conclusion: D and E are correct.
OCI documentation states: "OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features." A belongs to Vision, B and C aren't available-only D and E match OCI Language's capabilities.
Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".

NEW QUESTION # 99
You loaded data into Oracle Cloud Infrastructure (OCI) Data Science. To transform the data, you want to use the Accelerated Data Science (ADS) SDK. When you applied the get_recommendations() tool to the ADSDataset object, it showed you user-detected issues with all the recommended changes to apply to the dataset. Which option should you use to apply all the recommended transformations at once?

• A. visualize_transforms()
• B. get_transformed_dataset()
• C. auto_transform()
• D. fit_transform()

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Apply all recommended transformations from get_recommendations() in ADS.
* Understand ADS Tools: get_recommendations() suggests fixes (e.g., missing values).
* Evaluate Options:
* A: Returns transformed data-Not for applying-incorrect.
* B: Sklearn-style, not ADS-specific-incorrect.
* C: auto_transform()-Applies all recommendations-correct.
* D: Visualizes, doesn't apply-incorrect.
* Reasoning: auto_transform() executes the fixes suggested by get_recommendations().
* Conclusion: C is correct.
OCI documentation states: "After get_recommendations() identifies issues, use auto_transform() (C) on the ADSDataset to apply all recommended transformations at once." A retrieves, B is external, D visualizes- only C aligns with OCI's ADS transformation workflow.
Oracle Cloud Infrastructure ADS SDK Documentation, "Data Transformation Methods".

NEW QUESTION # 100
What does the Data Science Service template in Oracle Resource Manager (ORM) NOTautomatically create?

• A. Dynamic groups
• B. Individual Data Science users
• C. Required user groups
• D. Policies for a basic use case

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand ORM Template: It automates OCI Data Science setup with predefined configurations.
* Evaluate Components:
* A: User groups are created for role-based access-automated.
* B: Dynamic groups (e.g., for notebook sessions) are included-automated.
* C: Individual users require manual creation via IAM-not automated.
* D: Basic policies (e.g., access to Data Science resources) are included-automated.
* Reasoning: ORM focuses on infrastructure and permissions, not user accounts.
* Conclusion: C is the exception.
The OCI Resource Manager template for Data Science "automatically provisions user groups, dynamic groups, and policies for basic use cases," but "individual users must be created separately in IAM and assigned to groups." C is the only item not handled by the template, per the documentation.
Oracle Cloud Infrastructure Resource Manager Documentation, "Data Science Template".

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 adds a score to each signal indicating the probability that it's a false alarm
• B. It is used to disable the reporting of false alarms
• C. It determines how many false alarms occur before an error message is generated
• 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
Which OCI Data Science interaction method can function without the need of scripting?

• A. OCI Console
• B. REST APIs
• C. Language SDKs
• D. CLI

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI Data Science interaction method that doesn't require scripting.
* Understand Interaction Methods: OCI provides multiple ways to interact with Data Science services- some are GUI-based, others script-based.
* Evaluate Options:
* A. OCI Console: A web-based graphical interface allowing users to manage resources (e.g., create notebook sessions, deploy models) via point-and-click-no scripting needed.
* B. CLI: Command Line Interface requires writing commands (scripts) to execute tasks (e.g., oci data-science notebook-session create).
* C. Language SDKs: Software Development Kits (e.g., Python SDK) require coding to interact programmatically (e.g., oci.data_science.DataScienceClient).
* D. REST APIs: Application Programming Interfaces require scripted HTTP requests (e.g., using curl or a programming language).
* Reasoning: Only the OCI Console (A) offers a no-code, user-friendly interface, while B, C, and D rely on scripting or programming.
* Conclusion: A is the correct answer as it eliminates the need for scripting.
The OCI Console is described in the documentation as "a browser-based interface that allows users to manage OCI Data Science resources, such as creating notebook sessions or jobs, without writing code or scripts." In contrast, the CLI (B) requires command-line scripts, SDKs (C) need programming (e.g., Python), and REST APIs (D) involve scripted API calls. The Console's GUI distinguishes it as the only option functioning without scripting, aligning with Oracle's design for accessibility to non-programmers.
Oracle Cloud Infrastructure Data Science Documentation, "Getting Started with OCI Console" section.

NEW QUESTION # 103
You are a data scientist working for a manufacturing company. You have developed a forecasting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third-party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact. What file should be modified to include the missing libraries?

• A. score.py
• B. model_artifact_validate.py
• C. runtime.yaml
• D. requirements.txt

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Specify third-party libraries for model deployment.
* Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.
* Evaluate Options:
* A: Not a standard file-incorrect.
* B: Inference code-not for dependencies.
* C: Defines conda env with dependencies-correct.
* D: Pip list-not used in OCI conda deployments.
* Reasoning: runtime.yaml points to a conda env with all libraries.
* Conclusion: C is correct.
OCI documentation states: "In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model." score.py (B) is for logic, requirements.txt (D) isn't OCI-standard, and A doesn't exist-C fixes the issue.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".

NEW QUESTION # 104
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. Classification (single/multi-label)
• B. Key-point and landmark
• C. Polygonal segmentation
• D. Semantic segmentation
• E. Object detection
• F. Named entity extraction

Answer: A,D,E

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 # 105
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, 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
• 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, 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

Answer: D

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 # 106
As a data scientist, you use the Oracle Cloud Infrastructure (OCI) Language service to train custommodels.
Which types of custom models can be trained?

• A. Image classification, Named Entity Recognition (NER)
• B. Sentiment Analysis, Named Entity Recognition (NER)
• C. Text classification, Named Entity Recognition (NER)
• D. Object detection, Text classification

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify custom model types for OCI Language service.
* Understand OCI Language: Focuses on text analysis, not images.
* Evaluate Options:
* A: Image classification-Incorrect; Language is text-based.
* B: Text classification, NER-Both text tasks-correct.
* C: Sentiment-Pretrained, not custom; NER ok-incorrect.
* D: Object detection-Image-based, incorrect.
* Reasoning: B aligns with OCI Language's custom text capabilities.
* Conclusion: B is correct.
OCI documentation states: "OCI Language supports training custom models for text classification and Named Entity Recognition (NER) (B) using user data." A and D involve images, C includes pretrained sentiment- only B matches OCI Language's custom model scope.
Oracle Cloud Infrastructure Language Documentation, "Custom Model Types".

NEW QUESTION # 107
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. Console
• B. Git CLI
• C. ODSC CLI
• D. OCI Python SDK
• E. Oracle Cloud Infrastructure (OCI) Command Line Interface (CLI)
• F. Accelerated Data Science (ADS) Software Development Kit (SDK)

Answer: A,D,F

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 # 108
You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?

• A. The path to the archive.zip file
• B. The compartment of the Data Flow application
• C. The bucket used to read/write the PySpark script in Object Storage
• D. The display name of the application
• E. The local path to your PySpark script

Answer: B,C,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three required params for an OCI Data Flow app config.
* Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.
* Evaluate Options:
* A: Archive path-Optional if script is in Object Storage-incorrect.
* B: Local script path-Not needed; script is uploaded-incorrect.
* C: Compartment-Required for resource scope-correct.
* D: Bucket-Required for script storage/access-correct.
* E: Display name-Required for app identification-correct.
* Reasoning: C, D, E are mandatory metadata for Data Flow creation-script location is specified via bucket.
* Conclusion: C, D, E are correct.
OCI documentation states: "To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object." Local paths (B) or archives (A) are optional or handled separately-only C, D, E are required per OCI's Data Flow API spec.
Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".

NEW QUESTION # 109
Which two statements are true about published conda environments?

• A. In addition to service job run environment variables, conda environment variables can be used inData Science Jobs
• B. You can only create a published conda environment by modifying a Data Science conda environment
• C. The odsc conda init command is used to configure the location of published conda environments
• D. They are curated by Oracle Cloud Infrastructure (OCI) Data Science
• E. Your notebook session acts as the source to share published conda environments with team members

Answer: A,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify true statements about published conda environments in OCI.
* Understand Published Conda: Custom envs shared via Object Storage.
* Evaluate Options:
* A: False-Users create them, not OCI-curated.
* B: True-odsc conda init sets up access to published envs.
* C: False-Object Storage, not notebook, is the source.
* D: False-Can create from scratch, not just modifications.
* E: True-Conda env vars extend job configs.
* Reasoning: B configures access, E enhances flexibility-match OCI behavior.
* Conclusion: B and E are correct.
OCI documentation states: "odsc conda init (B) configures the bucket for published conda environments, and Jobs can use conda environment variables (E) alongside service vars." A misattributes curation, C misplaces source, and D limits creation-only B and E are true per OCI's conda management.
Oracle Cloud Infrastructure Data Science Documentation, "Published Conda Environments".

NEW QUESTION # 110
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