[Aug-2026] Verified 1Z0-1110-26 dumps Q&As - 1Z0-1110-26 dumps with Correct Answers
The Best Oracle Cloud Study Guide for the 1Z0-1110-26 Exam
NEW QUESTION # 96
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. Making a commit that is taking a snapshot of the local repository for the next push
- B. Pushing changes to a remote repository
- C. Moving the changes into staging area for the next commit
- D. Updating the local repo to match the content from a remote repository
- E. Converting an existing local project folder to a Git repository
Answer: B,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 # 97
As a data scientist for a hardware company, you have been asked to predict the revenue demand for the upcoming quarter. You develop a time series forecasting model to analyze the dat a. Select the correct sequence of steps to predict the revenue demand values for the upcoming quarter.
- A. Prepare model, deploy, verify, save, predict
- B. Verify, prepare model, deploy, save, predict
- C. Predict, deploy, save, verify, prepare model
- D. Prepare model, verify, save, deploy, predict
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Prepare Model: Build and train the time series model using historical data.
Verify: Validate the model’s accuracy (e.g., using metrics like MAE or RMSE).
Save: Store the trained model (e.g., in the OCI Model Catalog).
Deploy: Make the model available for predictions (e.g., via OCI Model Deployment).
Predict: Generate revenue forecasts for the upcoming quarter.
Evaluate Options: D follows this logical flow; others (e.g., A starts with “verify” before preparation) don’t.
In OCI Data Science, the workflow for time series forecasting involves preparing the model (training), verifying its performance, saving it to the catalog, deploying it, and then predicting. This sequence is standard for ML deployment in OCI, as per the documentation. (Oracle Cloud Infrastructure Data Science Documentation, "Time Series Forecasting Workflow").
NEW QUESTION # 98
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. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models
- B. Global and local behaviours of machine learning models are similar
- C. Local explanation techniques are model-agnostic, while global explanation techniques are not
- D. Global behaviour of a machine learning model may be complex, while the local behaviour may be approximated with a simpler surrogate model
Answer: D
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".
NEW QUESTION # 99
You have just started as a data scientist at a healthcare company. You have been asked to analyze and improve a deep neural network model, which was built based on the electrocardiogram records of patients. There are no details about the model framework that was built. What would be the best way to find more details about the machine learning models inside the model catalog?
- A. Check for metadata tags
- B. Check for model taxonomy details
- C. Check for provenance details
- D. Refer to the code inside the model
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.
Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.
Evaluate Options:
A . Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn’t typically include readable source code; it’s a trained object, not the training script.
B . Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.
C . Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).
D . Check for provenance details: Provenance tracks the model’s creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.
Reasoning: Provenance details are designed to document the “how” and “what” of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.
Conclusion: D is the best approach for detailed investigation.
In OCI Data Science, the Model Catalog stores provenance information, which includes “details about the model’s origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references.” This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.
NEW QUESTION # 100
You are asked to prepare data for a custom-built model that requires transcribing Spanish video recordings into a readable text format with profane words identified. Which Oracle Cloud Service would you use?
- A. OCI Language
- B. OCI Anomaly Detection
- C. OCI Translation
- D. OCI Speech
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Transcribe Spanish video audio and identify profanity.
Evaluate Options:
A: Anomaly Detection—Not for transcription or text analysis.
B: Speech—Converts audio to text (e.g., Spanish), base for further analysis—correct.
C: Translation—Translates text, not transcription.
D: Language—Analyzes text (e.g., profanity), but needs transcribed input.
Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.
Conclusion: B is correct for transcription.
OCI Speech “transcribes audio from video or audio files into text, supporting languages like Spanish.” Post-transcription, OCI Language could detect profanity, but B is the starting point—Anomaly (A) and Translation (C) don’t fit.
1: Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".
NEW QUESTION # 101
What is a conda environment?
- A. An environment deployment system on Oracle AI
- B. A system that manages package dependencies
- C. A collection of kernels
- D. An open-source environment management system
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Define Conda: Conda is a widely used tool for managing packages and environments in data science.
Evaluate Options:
A: Partially true—Conda manages dependencies, but it’s broader (an environment system).
B: Incorrect—Kernels (e.g., Jupyter) are separate; Conda manages environments.
C: Correct—Conda is an open-source tool for creating isolated environments with specific packages.
D: Incorrect—Not specific to Oracle AI; it’s a general tool.
Reasoning: C captures Conda’s full scope as an open-source system, beyond just dependency management (A).
Conclusion: C is the most accurate.
OCI documentation describes Conda as “an open-source package and environment management system that allows data scientists to create isolated environments with specific versions of Python and libraries.” A is too narrow, B misaligns with kernel concepts, and D ties it incorrectly to Oracle AI. C aligns with Conda’s official definition and OCI’s usage.
1: Oracle Cloud Infrastructure Data Science Documentation, "Conda Environments Overview".
NEW QUESTION # 102
Where do calls to stdout and stderr from score.py go in the model deployment?
- A. The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment
- B. The file that was defined for them on the virtual machine (VM)
- C. The OCI Console
- D. The OCI Cloud Shell, which can be accessed from the console
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate stdout/stderr output from score.py in deployment.
Understand Deployment: score.py runs in a model endpoint; logs are managed by OCI.
Evaluate Options:
A: False—No VM file is defined by default; logs go to OCI Logging.
B: False—Console displays UI, not raw logs.
C: False—Cloud Shell is a separate tool, not a log destination.
D: True—Predict logs in OCI Logging capture stdout/stderr.
Reasoning: OCI centralizes logs in its Logging service for deployments.
Conclusion: D is correct.
OCI documentation states: “During model deployment, stdout and stderr from score.py are automatically sent to the predict log in the OCI Logging service, as configured in the deployment settings.” A, B, and C don’t align with this managed logging approach—only D is accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Logging".
NEW QUESTION # 103
You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?
- A. The compartment of the Data Flow application
- B. The local path to your PySpark script
- C. The display name of the application
- D. The path to the archive.zip file
- E. The bucket used to read/write the PySpark script in Object Storage
Answer: A,C,E
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.
1: Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".
NEW QUESTION # 104
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.
1: Oracle Cloud Infrastructure Data Flow Documentation, "Overview".
NEW QUESTION # 105
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. Drift monitoring
- B. Operational monitoring
- C. Validate the model using recent data
- D. Redeploy the model
- E. Retrain the model
Answer: A,E
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".
NEW QUESTION # 106
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".
NEW QUESTION # 107
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. Sentiment analysis
- B. Punctuation correction
- C. Sentence diagramming
- D. Topic classification
- E. Table extraction
Answer: A,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.
1: Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".
NEW QUESTION # 108
You’re going to create an Oracle Cloud Infrastructure Anomaly Detection model for multivariate dat a. Where do you need to store the training data?
- A. Object Storage Bucket
- B. Your local machine
- C. Autonomous Data Warehouse
- D. MySQL database
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Anomaly Detection: This service trains models to detect anomalies in multivariate data (e.g., sensor readings), requiring data to be accessible within OCI’s ecosystem.
Assess Storage Requirements: The training data must be in a scalable, OCI-compatible location that the Anomaly Detection service can access programmatically.
Evaluate Options:
A . Your local machine: Data on a local machine isn’t directly accessible to OCI services without upload, making it impractical for cloud-based training.
B . MySQL database: While OCI supports MySQL, Anomaly Detection doesn’t natively integrate with it for training data; it prefers file-based input.
C . Autonomous Data Warehouse: This is a database for analytics, not the default storage for Anomaly Detection training data, which expects CSV/JSON files.
D . Object Storage Bucket: OCI Object Storage is a scalable, durable storage service that Anomaly Detection uses to ingest training data (e.g., CSV files).
Reasoning: Object Storage is the standard for large-scale data in OCI services, offering seamless integration with Anomaly Detection via APIs or SDKs.
Conclusion: D is the correct choice as it aligns with the service’s architecture.
The OCI Anomaly Detection service requires training data to be uploaded to an Object Storage Bucket in formats like CSV or JSON. This is explicitly outlined in the official documentation, which states that users must “upload the training dataset to an OCI Object Storage bucket” before creating a data asset for model training. Local storage (A) isn’t viable for cloud processing, and databases like MySQL (B) or Autonomous Data Warehouse (C) aren’t supported as primary inputs. Object Storage (D) provides the scalability and accessibility needed for multivariate anomaly detection workflows.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "Preparing Training Data" section.
NEW QUESTION # 109
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?
- A. It is trained on a combination of customer and general industry datasets
- B. It is an important tool for detecting fraud, network intrusions, and discrepancies in sensor time series analysis
- C. Accepted file types are SQL and Python
- D. Data used for analysis can be text or numerical in nature
Answer: B
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.
1: Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".
NEW QUESTION # 110
Which is NOT a valid OCI Data Science notebook session approach?
- A. Ensure you don’t execute long-running Python processes in a notebook cell
- B. Avoid having multiple users in the same notebook session due to the possibility of resource contention and write conflicts
- C. 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
- D. 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
- E. Run the process directly in the terminal and use Python logging to get updates on the progress of your job
Answer: C
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Best Practices".
NEW QUESTION # 111
Which statement about logs for Oracle Cloud Infrastructure Jobs is true?
- A. Each job run sends outputs to a single log for that job
- B. All stdout and stderr are automatically stored when automatic log creation is enabled
- C. Logs are automatically deleted when the job and job run is deleted
- D. Integrating data science jobs resources with logging is mandatory
Answer: B
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs Logging".
NEW QUESTION # 112
Which technique can be used for feature engineering in the machine learning lifecycle?
- A. K-means clustering
- B. Gradient boosting
- C. Principal Component Analysis (PCA)
- D. Support Vector Machines (SVM)
Answer: C
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".
NEW QUESTION # 113
How are datasets exported in the OCI Data Labeling service?
- A. As a line-delimited JSON file
- B. As an XML file
- C. As a binary file
- D. As a CSV file
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Data Labeling Export: After annotation, datasets are exported for ML use.
Check Supported Formats: OCI Data Labeling exports annotations in a structured, machine-readable format.
Evaluate Options:
A: Binary isn’t a standard export format for annotations.
B: XML isn’t used; JSON is preferred for flexibility.
C: Line-delimited JSON is the correct format, aligning with ML workflows.
D: CSV is common but not the default for OCI Data Labeling.
Conclusion: C matches the official export format.
OCI Data Labeling exports annotated datasets as line-delimited JSON files, which store each annotation as a separate JSON object per line, suitable for ML pipelines. This is explicitly stated in the documentation. (Oracle Cloud Infrastructure Data Labeling Service Documentation, "Exporting Datasets").
NEW QUESTION # 114
What is feature engineering in machine learning used for?
- A. To interpret ML models
- B. To transform existing features into new ones
- C. To perform parameter tuning
- D. To help understand the dataset features
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Define Feature Engineering: It’s the process of creating or modifying features to improve model performance.
Evaluate Options:
A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.
B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.
C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering—correct.
D: Understanding features occurs during exploration, not engineering.
Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of-week), distinct from tuning or interpretation.
Conclusion: C is the precise definition.
OCI Data Science documentation defines feature engineering as “the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy.” Examples include scaling or creating interaction terms, aligning 1with C. Other options (A, B, D) relate to different ML stages.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".
NEW QUESTION # 115
In which two ways can you improve data durability in Oracle Cloud Infrastructure Object Storage?
- A. Enable server-side encryption
- B. Enable Versioning
- C. Limit delete permissions
- D. Enable client-side encryption
- E. Setup volumes in a RAID1 configuration
Answer: B,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify two methods to enhance Object Storage durability.
Understand Durability: Ensures data isn’t lost—focus on redundancy and protection.
Evaluate Options:
A: RAID1—Block volume feature, not Object Storage.
B: Encryption—Secures data, not durability.
C: Versioning—Retains old versions, prevents loss—correct.
D: Limit delete—Prevents accidental deletion—correct.
E: Client encryption—Secures, not durability-focused.
Reasoning: C and D directly protect against data loss—durability-focused.
Conclusion: C and D are correct.
OCI documentation states: “Improve Object Storage durability with Versioning (C) to retain previous object versions and by limiting delete permissions (D) to prevent accidental loss.” A isn’t applicable, B and E focus on security—only C and D enhance durability per OCI’s storage features.
1: Oracle Cloud Infrastructure Object Storage Documentation, "Data Durability Options".
NEW QUESTION # 116
Which OCI service enables you to build, train, and deploy machine learning models in the cloud?
- A. Oracle Cloud Infrastructure Data Integration
- B. Oracle Cloud Infrastructure Data Catalog
- C. Oracle Cloud Infrastructure Data Flow
- D. Oracle Cloud Infrastructure Data Science
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for ML model lifecycle.
Evaluate Options:
A: Data Catalog—Metadata management, not ML.
B: Data Integration—ETL, not ML.
C: Data Science—Full ML lifecycle—correct.
D: Data Flow—Spark processing, not full ML.
Reasoning: C supports building, training, deploying models.
Conclusion: C is correct.
OCI documentation states: “OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog.” A, B, and D serve other purposes—only C fits the ML lifecycle per OCI’s offerings.
1: Oracle Cloud Infrastructure Data Science Documentation, "Service Overview".
NEW QUESTION # 117
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