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NEW QUESTION # 53
Which Oracle Cloud Infrastructure (OCI) Data Science policy is invalid?
- A. Allow group DataScienceGroup to use data-science-model-sessions in compartment DataScience
- B. Allow group DataScienceGroup to use virtual-network-family in compartment DataScience
- C. Allow dynamic-group DataScienceDynamicGroup to manage data-science-projects in compartment DataScience
- D. Allow dynamic-group DataScienceDynamicGroup to manage data-science-family in compartment DataScience
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Spot the invalid OCI IAM policy.
* Understand Policies: Use verbs (e.g., use, manage) and resource types.
* Evaluate Options:
* A: Valid-virtual-network-family is a resource type.
* B: Invalid-No data-science-model-sessions resource type; likely meant data-science-models.
* C: Valid-data-science-projects is correct.
* D: Valid-data-science-family aggregates Data Science resources.
* Reasoning: B uses a nonexistent resource type-error in syntax.
* Conclusion: B is incorrect.
OCI IAM documentation lists valid resource types: "data-science-models, data-science-projects, data-science- family-but not data-science-model-sessions (B)." A, C, D are correct-B is invalid due to this typo
/misunderstanding.
Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".
NEW QUESTION # 54
For your next data science project, you need access to public geospatial images. Which Oracle Cloud service provides free access to those images?
- A. Oracle Cloud Infrastructure (OCI) Data Science
- B. Oracle Big Data Service
- C. Oracle Open Data
- D. Oracle Analytics Cloud
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find the OCI service for free geospatial images.
* Evaluate Options:
* A: Big Data Service-Spark processing, not datasets.
* B: Analytics Cloud-Visualization, not data source.
* C: Data Science-ML platform, not dataset provider.
* D: Open Data-Free public datasets, including geospatial-correct.
* Reasoning: Open Data is OCI's public dataset hub.
* Conclusion: D is correct.
OCI documentation states: "Oracle Open Data provides free access to curated datasets, including geospatial images, for public use." A, B, and C serve other purposes-only D delivers free geospatial data.
Oracle Cloud Infrastructure Open Data Documentation, "Dataset Offerings".
NEW QUESTION # 55
True or false? Bias is a common problem in data science applications.
- A. True
- B. False
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Assess if bias is a common issue in data science.
* Define Bias: Systematic errors in data/models (e.g., skewed training data).
* Evaluate Statement:
* Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws-widely recognized in ML.
* Examples: Gender bias in hiring models, racial bias in facial recognition.
* Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.
* Conclusion: A (True) is correct.
OCI documentation notes: "Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks." This aligns with industry standards-bias is a well-documented issue, making A true.
Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".
NEW QUESTION # 56
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 job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
- C. 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
- D. 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
Answer: B
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 # 57
Which Security Zone policy is NOT valid?
- A. Resources in a security zone should not be accessible from the public internet
- B. A boot volume can be moved from a security zone to a standard compartment
- C. A compute instance cannot be moved from a security zone to a standard compartment
- D. Resources in a security zone must be automatically backed up regularly
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Spot the invalid Security Zone policy.
* Understand Security Zones: Enforce strict security-resources can't leave easily.
* Evaluate Options:
* A: Boot volume move-Not allowed; violates zone rules-invalid.
* B: Compute no-move-True, enforces security.
* C: No public access-True, standard policy.
* D: Auto-backup-True, required for compliance.
* Reasoning: A contradicts Security Zone immutability.
* Conclusion: A is incorrect.
OCI documentation states: "Resources like boot volumes (A) cannot be moved from a Security Zone to a standard compartment-B, C, and D are enforced policies." A violates OCI's Security Zone restrictions- only A is not valid.
Oracle Cloud Infrastructure Security Zones Documentation, "Policy Restrictions".
NEW QUESTION # 58
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 data. Select the correct sequence of steps to predict the revenue demand values for the upcoming quarter.
- A. Prepare model, verify, save, deploy, predict
- B. Verify, prepare model, deploy, save, predict
- C. Predict, deploy, save, verify, prepare model
- D. Prepare model, deploy, verify, save, predict
Answer: A
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. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "Time Series Forecasting Workflow").
NEW QUESTION # 59
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. Notebook Examples
- D. Command Palette
- E. Environment Explorer
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 # 60
Which OCI cloud service lets you centrally manage the encryption keys that protect your data and the secret credentials that you use to securely access resources?
- A. Data Safe
- B. Cloud Guard
- C. Vault
- D. Data Guard
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for key and secret management.
* Evaluate Options:
* A: Data Safe-Database security, not key management.
* B: Cloud Guard-Threat detection, not keys.
* C: Data Guard-DB replication, not keys.
* D: Vault-Key and secret management-correct.
* Reasoning: Vault is OCI's dedicated service for crypto keys and secrets.
* Conclusion: D is correct.
OCI documentation states: "OCI Vault (D) centrally manages encryption keys and secrets, securing data and resource access." A, B, and C serve other purposes-only D matches per OCI's securityservices.
Oracle Cloud Infrastructure Vault Documentation, "Overview".
NEW QUESTION # 61
As a data scientist, you create models for cancer prediction based on mammographic images. The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix. Which model would you prefer and why?
* Model 1 has Test accuracy is 80% and recall is 70%
* Model 2 has Test accuracy is 75% and recall is 85%
- A. Model 1, because the test accuracy is high
- B. Model 2, because recall is high
- C. Model 1, because recall has lesser impact on predictions in this use case
- D. Model 2, because recall has more impact on predictions in this use case
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Choose the better model for cancer prediction based on metrics.
* Understand Metrics:
* Accuracy: Overall correct predictions.
* Recall: True positives / (True positives + False negatives)-crucial for cancer (minimizing misses).
* Context: Cancer prediction prioritizes recall-false negatives (missed cancers) are critical.
* Evaluate Models:
* Model 1: 80% accuracy, 70% recall-Misses more cancers.
* Model 2: 75% accuracy, 85% recall-Misses fewer cancers.
* Evaluate Options:
* A: High recall-True, but lacks context.
* B: High accuracy-Misses recall's importance.
* C: Recall's impact-Correct for cancer use case-best.
* D: Lesser recall impact-Incorrect for this priority.
* Reasoning: C emphasizes recall's critical role-aligns with medical needs.
* Conclusion: C is correct.
OCI documentation advises: "For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at
85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C)." A is partial, B overlooks context, D reverses priority-only C fits OCI's ML evaluation guidance for this scenario.
Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".
NEW QUESTION # 62
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. correlation_ratio_plot()
- B. pearson_plot()
- C. cramersv_plot()
- D. corr()
Answer: A
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 # 63
You want to write a Python script to create a collection of different projects for your data science team. Which Oracle Cloud Infrastructure (OCI) Data Science interface would you use?
- A. Command Line Interface (CLI)
- B. OCI Console
- C. The OCI Software Development Kit (SDK)
- D. Mobile App
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Choose an interface for a Python script to manage projects.
* Evaluate Options:
* A: OCI SDK-Python-based, scriptable-correct.
* B: Console-GUI, not scriptable.
* C: CLI-Command-based, not Python-native.
* D: Mobile App-Not for scripting.
* Reasoning: A enables programmatic project creation.
* Conclusion: A is correct.
OCI documentation states: "Use the OCI Python SDK (A) to programmatically manage Data Science resources, like creating projects, via Python scripts." B, C, and D don't support Python scripting-only A fits.
Oracle Cloud Infrastructure SDK Documentation, "Data Science API".
NEW QUESTION # 64
Why is data sampling useful for data scientists?
- A. It lets them analyze datasets in small batches to reduce their use of system resources.
- B. It reduces the amount of data storage space that's required for data science applications.
- C. It enables them to use a representative subset of data to build accurate analytical models more quickly.
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine the primary benefit of data sampling.
* Define Sampling: Selecting a subset of data to represent the whole-used in ML/statistics.
* Evaluate Options:
* A: Small batches reduce resources-True but not the main purpose.
* B: Reduces storage-Incidental, not the goal.
* C: Representative subset for faster, accurate models-Core purpose of sampling.
* Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).
* Conclusion: C is correct.
OCI documentation states: "Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical." A focuses on resources (secondary), B on storage (not primary)-only C captures the analytical intent per OCI's AutoML sampling approach.
Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".
NEW QUESTION # 65
You have an image classification model in the model catalog which is deployed as an HTTP endpoint using model deployments. Your tenancy administrator is seeing increased demands and has asked you to increase the load balancing bandwidth from the default of 10Mbps. You are provided with the following information:
* Payload size in KB = 1024
* Estimated requests per second = 120 requests/second (Monday through Friday, in every month, in every year)
* Buffer percentage = 20%What is the optimal load balancing bandwidth to redeploy your model?
- A. 452 Mbps
- B. 1152 Mbps
- C. 7052 Mbps
- D. 52 Mbps
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Calculate optimal bandwidth for model deployment.
* Given Data:
* Payload size = 1024 KB = 1024 * 8 = 8192 Kb (kilobits).
* Requests/sec = 120.
* Buffer = 20% = 0.2.
* Calculate Base Bandwidth:
* Bits/sec = Payload * Requests = 8192 Kb * 120 = 983,040 Kb/s = 983.04 Mbps.
* Add Buffer:
* Total = Base * (1 + Buffer) = 983.04 * 1.2 = 1179.648 Mbps.
* Evaluate Options: Closest to 1179.648 Mbps is 1152 Mbps (D)-realistic rounding.
* Conclusion: D is correct.
OCI documentation advises: "Calculate bandwidth as payload size (in bits) * requests/sec, then add a buffer (e.
g., 20%) for peak loads." Here, 1024 KB = 8192 Kb, * 120 = 983.04 Mbps, * 1.2 = 1179.648 Mbps. D (1152 Mbps) is the closest practical option-452 (A) and 52 (B) are too low, 7052 (C) excessive.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Load Balancing".
NEW QUESTION # 66
Which statement is true about origin management in Web Application Firewall (WAF)?
- A. Both the statements are false
- B. Only statement B is true
- C. Both the statements are true
- D. Only a single origin can be active for a WAF
- E. Only statement A is true
- F. Multiple origins can be defined
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine truth about WAF origin management.
* Understand WAF: Protects apps by routing traffic via origins.
* Evaluate Statements:
* A: Multiple origins-True; WAF supports this.
* B: Single active origin-True; only one is active per policy.
* Evaluate Options:
* C: B only-False; A is true.
* D: Both false-Incorrect.
* E: Both true-Correct per OCI WAF.
* F: A only-False; B is true.
* Conclusion: E is correct.
OCI documentation states: "WAF allows defining multiple origins (A), but only one origin is active per WAF policy at a time (B)-both are true (E)." C, D, and F misalign-E matches OCI's WAF origin management.
Oracle Cloud Infrastructure WAF Documentation, "Origin Management".
NEW QUESTION # 67
Which is NOT a part of Observability and Management Services?
- A. Logging
- B. Logging Analytics
- C. Event Services
- D. OCI Management Service
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the non-Observability and Management (O&M) service in OCI.
* Understand O&M: Includes monitoring, logging, events tools.
* Evaluate Options:
* A: Event Services-Triggers actions, part of O&M-correct.
* B: OCI Management Service-Not a defined O&M service-incorrect.
* C: Logging Analytics-Log analysis, O&M component-correct.
* D: Logging-Log collection, O&M component-correct.
* Reasoning: B isn't listed in OCI's O&M suite-others are.
* Conclusion: B is correct (not part of O&M).
OCI documentation lists "Observability and Management Services as including Event Services (A), Logging Analytics (C), and Logging (D)-'OCI Management Service' (B) is not a recognized component." B appears to be a misnomer-only A, C, D are O&M per OCI's service catalog.
Oracle Cloud Infrastructure Observability and Management Documentation, "Service Overview".
NEW QUESTION # 68
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. 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
- B. 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
- C. 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
- D. 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
Answer: C
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 # 69
Which TWO statements about Oracle Cloud Infrastructure (OCI) Open Data service are true?
- A. Open Data includes text and image data repositories for AI and ML.
- B. Audio and video formats are not available.
- C. Open Data is a dataset repository made for the people that create, use, and manipulate datasets.
- D. Subscribers can pay and log into Open Data to view curated datasets that are otherwise not available to the public.
- E. A primary goal of Open Data is for users to contribute to the data repositories to expand the content offered.
- F. Each dataset in Open Data consists of code and tooling usage examples for consumption and reproducibility.
Answer: A,C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Analyze OCI Open Data: OCI Open Data is a free service providing access to public datasets for AI
/ML use cases.
* Evaluate Statements:
* A: True-Open Data includes text and image datasets (e.g., geospatial images).
* B: False-Video and other formats may be available depending on the dataset; no strict exclusion exists.
* C: False-Datasets may include metadata, but code/tooling examples aren't guaranteed.
* D: True-It's designed for data scientists and analysts who work with datasets.
* E: False-It's not a user-contributed repository; it's curated by Oracle.
* F: False-Open Data is free and public, not subscription-based.
* Select Two: A and D align with the service's purpose and offerings.
OCI Open Data provides access to datasets like text and images (A) for AI/ML, aimed at data professionals (D). It's a free, curated service, not user-contributed (E) or paid (F), and while it focuses on certain formats, it doesn't explicitly exclude audio/video (B). (Reference: Oracle Cloud Infrastructure Open Data Documentation, "Overview of Open Data").
NEW QUESTION # 70
You are working as a data scientist for a healthcare company. They decide 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 stepsto develop a PySpark application in Oracle Cloud Infrastructure (OCI) Data Science?
- A. 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
- B. 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
- C. 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
- D. 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
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence steps for PySpark app development.
* Steps:
* Launch notebook: First-sets up environment.
* Install PySpark conda: Second-adds Spark libraries.
* Configure core-site.xml: Third-connects to data.
* Develop app: Fourth-writes code.
* Data Flow: Fifth-scales (optional).
* Evaluate: D (1, 2, 3, 4, 5) matches this logical order.
* Reasoning: Notebook first, then setup and coding.
* Conclusion: D is correct.
OCI documentation states: "1) Launch a notebook session, 2) install a PySpark conda env, 3) configure core- site.xml, 4) develop your PySpark app, 5) optionally use Data Flow (D)." Other orders (A, B, C) misplace notebook launch or config-D is correct.
Oracle Cloud Infrastructure Data Science Documentation, "PySpark Development".
NEW QUESTION # 71
Arrange the following in the correct Git Repository workflow order:
* Install, configure, and authenticate Git.
* Configure SSH keys for the Git repository.
* Create a local and remote Git repository.
* Commit files to the local Git repository.
* Push the commit to the remote Git repository.
- A. 4, 2, 3, 1, 5
- B. 1, 2, 3, 4, 5
- C. 3, 5, 1, 2, 4
- D. 2, 3, 1, 4, 5
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Step 1: Install, configure, and authenticate Git: Git must be installed and configured (e.g., git config -- global user.name) before any repository actions.
* Step 2: Configure SSH keys: SSH keys are set up (e.g., ssh-keygen) and added to the remote service (e.
g., GitHub, OCI Code Repository) for secure access.
* Step 3: Create local and remote Git repository: Initialize a local repo (git init) and create/link a remote repo (e.g., git remote add origin).
* Step 4: Commit files: Add files (git add .) and commit them locally (git commit -m "message").
* Step 5: Push to remote: Push local commits to the remote repo (git push origin main).
* Evaluate Options: Only D (1, 2, 3, 4, 5) follows this logical sequence; others (e.g., A starts with SSH before Git installation) are illogical.
The standard Git workflow in OCI Data Science or general practice begins with installing Git (1), configuring SSH for secure access (2), creating repositories (3), committing locally (4), and pushing remotely (5). The OCI Code Repository documentation aligns with this: "First, install Git and configure authentication (e.g., SSH), then set up repositories and manage code." D is the only option reflecting this industry-standard process.
Oracle Cloud Infrastructure Code Repository Documentation, "Git Workflow Basics".
NEW QUESTION # 72
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image data. Which of the following THREE are possible ways to annotate an image in Data Labeling?
- A. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image
- B. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
- C. Adding labels to an image using object detection, by drawing bounding boxes to an image
- D. Adding a single label to an image
- E. Adding multiple labels to an image
Answer: C,D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three annotation methods in OCI Data Labeling for images.
* Understand Data Labeling: Supports image annotations for ML.
* Evaluate Options:
* A: Semantic segmentation with boxes-Incorrect; segmentation is pixel-based, not boxes.
* B: Single label (classification)-Supported-correct.
* C: No bounding boxes-False; boxes are supported.
* D: Object detection with boxes-Supported-correct.
* E: Multiple labels (multi-label)-Supported-correct.
* Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.
* Conclusion: B, D, E are correct.
OCI documentation states: "Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E)." A misdefines segmentation, C contradicts support-only B, D, E are valid per OCI's Data Labeling features.
Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".
NEW QUESTION # 73
What is feature engineering in machine learning used for?
- A. To help understand the dataset features
- B. To interpret ML models
- C. To perform parameter tuning
- D. To transform existing features into new ones
Answer: D
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 with C. Other options (A, B, D) relate to different ML stages.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".
NEW QUESTION # 74
Which Oracle Cloud Service provides restricted access to target resources?
- A. Bastion
- B. Load Balancer
- C. SSL Certificate
- D. Internet Gateway
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for restricted resource access.
* Evaluate Options:
* A: Bastion-Secure, temporary access to resources-correct.
* B: Internet Gateway-Public access, not restricted.
* C: Load Balancer-Distributes traffic, not access control.
* D: SSL Certificate-Secures comms, not access.
* Reasoning: Bastion limits access (e.g., SSH) to specific targets.
* Conclusion: A is correct.
OCI documentation states: "OCI Bastion (A) provides restricted, audited access to target resources like instances, typically via SSH." B, C, and D don't restrict-only A fits per OCI's security services.
Oracle Cloud Infrastructure Bastion Documentation, "Overview".
NEW QUESTION # 75
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? Which THREE steps would you perform?
- A. Reduce the load balancer bandwidth limit so that fewer requests come in
- B. Delete the deployment
- C. Update the deployment to add more instances
- D. Update the deployment to use fewer instances
- E. Update the deployment to use a larger virtual machine (more CPUs/memory)
Answer: A,C,E
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Prevent deployment failure due to utilization limits.
* Understand Utilization: High load requires capacity or throttling.
* Evaluate Options:
* A: More instances-Scales horizontally-correct.
* B: Delete-Stops service, not a fix-incorrect.
* C: Fewer instances-Worsens issue-incorrect.
* D: Larger VM-Scales vertically-correct.
* E: Reduce bandwidth-Controls load-correct.
* Reasoning: A and D increase capacity, E manages demand-effective trio.
* Conclusion: A, D, E are correct.
OCI documentation states: "To avoid utilization limits, increase instances (A), use a larger compute shape (D), or reduce load balancer bandwidth (E) to manage request rates." B stops service, C reduces capacity- only A, D, E align with OCI's deployment scaling options.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".
NEW QUESTION # 76
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