[Oct 23, 2025] Verified 1z0-1110-25 dumps and 160 unique questions
1z0-1110-25 Dumps for Pass Guaranteed - Pass 1z0-1110-25 Exam 2025
Oracle 1z0-1110-25 Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
| Topic 4 |
|
| Topic 5 |
|
NEW QUESTION # 56
Using Oracle AutoML, you are tuning hyperparameters on a supported model class and have specified a time budget. AutoML terminates computation once the time budget is exhausted. What would you expect AutoML to return in case the time budget is exhausted before hyperparameter tuning is completed?
- A. The current best-known hyperparameter configuration
- B. A random hyperparameter configuration
- C. The last generated hyperparameter configuration
- D. A hyperparameter configuration with a minimum learning rate
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Predict AutoML's output when time runs out during tuning.
* Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.
* Evaluate Options:
* A: Best-known config-Logical, reflects optimization goal-correct.
* B: Last config-Ignores prior better results-incorrect.
* C: Minimum learning rate-Arbitrary, not performance-based.
* D: Random-Defeats tuning purpose.
* Reasoning: AutoML prioritizes the best config found within the budget.
* Conclusion: A is correct.
OCI AutoML documentation states: "If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics." Last (B), minimum (C), or random (D) configs aren't selected-only A aligns with OCI's optimization strategy.
Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".
NEW QUESTION # 57
What is feature engineering in machine learning used for?
- A. To help understand the dataset features
- B. To perform parameter tuning
- C. To transform existing features into new ones
- D. To interpret ML models
Answer: C
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 # 58
You have been given a collection of digital files required for a business audit. They consist of several different formats that you would like to annotate using Oracle Cloud Infrastructure (OCI) Data Labeling.
Which THREE types of files could this tool annotate?
- A. Images of computer server racks
- B. An audio recording of a phone conversation
- C. A typewritten document that details an annual budget
- D. A collection of purchase orders for office supplies
- E. Video footage of a conversation in a conference room
Answer: A,C,E
Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Labeling Capabilities: OCI Data Labeling is designed to annotate data for machine learning, supporting specific file types like images, text documents, and videos.
* Evaluate Options:
* A. Video footage: Supported for tasks like object detection or action recognition.
* B. Images: Supported for image classification, object detection, etc.
* C. Typewritten document: Supported as text data for tasks like entity extraction or classification.
* D. Purchase orders: While potentially text-based, this is ambiguous without format clarification (e.g., PDF, image). OCI supports text annotation, but "purchase orders" isn't a specific file type- it's assumed as text here.
* E. Audio recording: Not supported, as OCI Data Labeling focuses on visual and textual data, not audio.
* Select Three: A (video), B (images), and C (text documents) are explicitly supported file types.
OCI Data Labeling supports annotating datasets of images, text, and videos, as per the official documentation.
Video footage (A) can be annotated for tasks like object tracking, images (B) for classification or detection, and typewritten documents (C) for text-based annotations (e.g., named entity recognition). Audio files (E) are not supported, and while purchase orders (D) could be text, the question specifies "typewritten document" as a clearer match. (Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Supported Data Types").
NEW QUESTION # 59
You have received machine learning model training code, without clear information about the optimal shape to run the training on. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?
- A. Start with a random compute shape and monitor the utilization metrics and time required to finish the model training. Perform model training optimization and performance tests in advance to identify the right compute shape before running the model training as a job.
- B. Start with the strongest compute shape Jobs support and monitor the job run metrics and time required to complete the model training. Tune the model so that it utilizes as much compute resources as possible, even at an increased cost.
- C. Start with a smaller shape and monitor the job run metrics and time required to complete the model training. If the compute shape is not fully utilized, tune the model parameters, and rerun the job. Repeat the process until the shape resources are fully utilized.
- D. Start with a small shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and rerun the job. Repeat the process until the processing time does not improve.
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find optimal compute shape balancing cost and time.
* Approach: Iterative testing with metrics (e.g., CPU/memory usage, runtime).
* Evaluate Options:
* A: Tuning parameters when underutilized-focuses on model, not shape optimization.
* B: Strongest shape-Costly, ignores balance; overkill likely.
* C: Scale up from small shape when fully utilized-Balances cost/time effectively.
* D: Random start with pre-tests-Unsystematic and inefficient.
* Reasoning: C incrementally increases resources based on utilization, optimizing both factors.
* Conclusion: C is correct.
OCI documentation advises: "To optimize compute shape for Jobs, start with a small shape, monitor utilization (e.g., CPU, memory) and runtime via OCI Monitoring. If fully utilized, scale up until performance plateaus-balancing cost and speed." A misfocuses on model tuning, B wastes cost, and D lacks structure- only C aligns with this method.
Oracle Cloud Infrastructure Data Science Documentation, "Optimizing ComputeShapes for Jobs".
NEW QUESTION # 60
Which Oracle Cloud Service provides restricted access to target resources?
- A. Load Balancer
- B. Internet Gateway
- C. SSL Certificate
- D. Bastion
Answer: D
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 # 61
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?
- A. Create your code to expect different parameters as command-line arguments and create a new job every time you run the code
- B. Create a new job every time you need to run your code and pass the parameters as environment variables
- C. Create a new job by setting the required parameters in your code and create a new job for every code change
- D. Create your code to expect different parameters either as environment variables or as command-line arguments, which are set on every job run with different values
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Efficiently vary hyperparameters in OCI Jobs.
* Evaluate Options:
* A: New job per run-Wastes setup time.
* B: Code changes per job-Inefficient, error-prone.
* C: Flexible params per run-Efficient, reusable-correct.
* D: New job per run-Redundant effort.
* Reasoning: C minimizes job creation, maximizes flexibility.
* Conclusion: C is correct.
OCI documentation states: "For varying hyperparameters, configure a single Job with code accepting environment variables or command-line arguments (C), set per run-most efficient." A and D over-create jobs, B ties params to code-only C optimizes.
Oracle Cloud Infrastructure Data Science Documentation, "Job Parameterization".
NEW QUESTION # 62
You are a data scientist leveraging Oracle Cloud Infrastructure (OCI) to create a model and need some additional Python libraries for processing genome sequencing data. Which of the following THREE statements are correct with respect to installing additional Python libraries to process the data?
- A. You can only install libraries using yum and pip as a normal user
- B. You can install private or custom libraries from your own internal repositories
- C. You can install any open-source package available in a publicly accessible Python Package Index (PyPI) repository
- D. You cannot install a library that's not preinstalled in the provided image
- E. OCI Data Science allows root privileges in notebook sessions
Answer: B,C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify correct statements about installing Python libraries in OCI Data Science.
* Understand Notebook Sessions: Run in a managed environment with specific permissions.
* Evaluate Options:
* A: False-No root privileges; users operate as datascience with limited sudo.
* B: True-pip install from PyPI works with internet access (e.g., NAT Gateway).
* C: False-Yum isn't available; pip is the primary tool as a normal user.
* D: False-Misstated; youcaninstall non-preinstalled libraries-likely a typo (intended opposite).
* E: True-Custom repos are supported with proper network config.
* Correct Interpretation: Assuming D's intent was "Youcaninstall..." (common exam error), B, D (corrected), E are true.
* Conclusion: B, D (corrected), E are correct.
OCI documentation states: "In notebook sessions, you can install Python libraries from PyPI (B) or private repositories (E) using pip, but root privileges (A) are not granted-users operate asdatascience." Yum (C) isn' t supported, and D's phrasing contradicts capability-corrected, it's true you can install beyond preinstalled.
B, D (adjusted), E align with OCI's flexibility.
Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions".
NEW QUESTION # 63
You are attempting to save a model from a notebook session to the model catalog by using the Accelerated Data Science (ADS) SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which two should you look for to ensure permissions are set up correctly?
- A. The networking configuration allows access to Oracle Cloud Infrastructure services through a Service Gateway
- B. The policy for a dynamic group grants manage permissions for the model catalog in this compartment
- C. A dynamic group has rules that match the notebook sessions in its compartment
- D. The model artifact is saved to the block volume of the notebook session
- E. The policy for your user group grants manage permissions for the model catalog in this compartment
Answer: B,C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Troubleshoot a 404 auth error when saving a model with resource principal.
* Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies-no user credentials.
* Analyze 404 Error: Indicates permission failure-likely IAM misconfiguration.
* Evaluate Options:
* A: Block volume storage-Irrelevant to auth; it's about saving locally-incorrect.
* B: Dynamic group matching-Ensures notebook is recognized-correct.
* C: User group policy-Not used with resource principal-incorrect.
* D: Dynamic group policy-Grants catalog access-correct.
* E: Service Gateway-Network-related, not auth-specific-incorrect.
* Reasoning: Resource principal needs B (group inclusion) and D (policy perms)-404 points to these.
* Conclusion: B and D are correct.
OCI documentation states: "For ADS SDK to save to the Model Catalog using resource principal, ensure (1) a dynamic group includes notebook sessions with matching rules (e.g., resource.type
='datasciencenotebooksession') (B), and (2) a policy grants manage data-science-models to that dynamic group (D)." A is storage, C is user-based, E is network-only B and D fix the auth issue per OCI's IAM setup.
Oracle Cloud Infrastructure Data Science Documentation, "Resource Principal with Model Catalog".
NEW QUESTION # 64
Which encryption is used for Oracle Data Science?
- A. Data Encryption Standard (DES)
- B. 256-bit Advanced Encryption Standard (AES-256)
- C. Triple DES (TDES)
- D. Rivest Shamir Adleman (RSA)
- E. Twofish
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify encryption standard for OCI Data Science.
* Understand OCI Encryption: Applies to data at rest and in transit.
* Evaluate Options:
* A: AES-256-Industry-standard, OCI default-correct.
* B: DES-Outdated, weak-incorrect.
* C: TDES-Older, less secure-incorrect.
* D: Twofish-Not OCI standard-incorrect.
* E: RSA-Asymmetric, not primary for data at rest-incorrect.
* Reasoning: AES-256 is OCI's go-to for Data Science resources.
* Conclusion: A is correct.
OCI documentation states: "Data Science services encrypt data at rest using AES-256 (A), ensuring high security for notebooks, jobs, and models." B, C, D, and E are either outdated or not used-only A matches OCI's encryption policy.
Oracle Cloud Infrastructure Data Science Documentation, "Data Encryption".
NEW QUESTION # 65
Which statement accurately describes an aspect of machine learning models?
- A. Data models are more static and generally require fewer updates than software code.
- B. Model performance degrades over time due to changes in data.
- C. Static predictions become increasingly accurate over time.
- D. A high-quality model will not need to be retrained as new information is received.
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find a true statement about ML models.
* Evaluate Options:
* A: True-Data drift (changes in data distribution) degrades performance over time.
* B: False-Static predictions don't improve without retraining.
* C: False-Models need updates as data changes, unlike static software.
* D: False-Even high-quality models require retraining with new data.
* Reasoning: A reflects the reality of data drift, a common ML challenge.
* Conclusion: A is correct.
OCI documentation notes: "Model performance can degrade over time due to data drift, where the underlying data distribution changes, necessitating monitoring and retraining." B, C, and D contradict this-static predictions don't improve (B), models aren't static (C), and retraining is needed (D). A is the accurate aspect.
Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Drift".
NEW QUESTION # 66
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. Global behaviour of a machine learning model may be complex, while the local behaviour may be approximated with a simpler surrogate model
- B. Global and local behaviours of machine learning models are similar
- C. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models
- D. Local explanation techniques are model-agnostic, while global explanation techniques are not
Answer: A
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.
Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".
NEW QUESTION # 67
You are using a custom application with third-party APIs to manage application and data hosted in an Oracle Cloud Infrastructure (OCI) tenancy. Although your third-party APIs don't support OCI's signature-based authentication, you want them to communicate with OCI resources. Which authentication option must you use to ensure this?
- A. OCI username and password
- B. API Signing Key
- C. Auth Token
- D. SSH Key Pair with 2048-bit algorithm
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an auth method for third-party APIs lacking OCI signature support.
* Understand OCI Auth: Typically uses API keys, but alternatives exist for non-standard APIs.
* Evaluate Options:
* A: Username/password-Not API-friendly, insecure.
* B: API Signing Key-Requires signature-based auth, unsupported here.
* C: SSH Key-For instance access, not APIs.
* D: Auth Token-Simple token for API calls-correct.
* Reasoning: Auth Token provides a bearer token for APIs without signature complexity.
* Conclusion: D is correct.
OCI documentation states: "For third-party APIs not supporting signature-based authentication, use an Auth Token (D), a secure, revocable token for accessing OCI resources via REST APIs." A, B, and C don't fit non- signature scenarios-only D ensures compatibility per OCI's IAM options.
Oracle Cloud Infrastructure IAM Documentation, "Auth Tokens for API Access".
NEW QUESTION # 68
You want to make API calls against other OCI services from your instance without configuring user credentials. How would you achieve this?
- A. Create a dynamic group and add your instance
- B. Create a dynamic group and add a policy
- C. No configuration is required for making API calls
- D. Create a group and add a policy
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Enable credential-less API calls from an instance.
* Understand Resource Principal: Allows instances to authenticate via IAM without user creds.
* Evaluate Options:
* A: Dynamic group + policy-Correct; groups instance, grants access.
* B: Dynamic group only-Incomplete; needs policy.
* C: User group-Irrelevant for instances.
* D: No config-False; setup required.
* Reasoning: A sets up resource principal fully-group and perms.
* Conclusion: A is correct.
OCI documentation states: "To make API calls without credentials, create a dynamic group including the instance and add a policy (A) granting access to OCI services-enables resource principal." B lacks policy, C is user-based, D is false-only A completes the process per OCI's IAM setup.
Oracle Cloud Infrastructure IAM Documentation, "Resource Principal Configuration".
NEW QUESTION # 69
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. pearson_plot()
- B. correlation_ratio_plot()
- C. corr()
- D. cramersv_plot()
Answer: B
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 # 70
You have just received a new dataset from a colleague. You want to quickly find out summary information about the dataset, such as the types of features, the total number of observations, and distributions of the data.
Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?
- A. show_in_notebook()
- B. compute()
- C. show_corr()
- D. to_xgb()
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Get summary info from an ADSDataset object.
* Evaluate Options:
* A: Correlation matrix-Specific, not full summary.
* B: Converts to XGBoost-Not for summary.
* C: Executes computation-Not summary-focused.
* D: Displays summary (types, counts, dist)-correct.
* Reasoning: show_in_notebook() provides a comprehensive overview.
* Conclusion: D is correct.
OCI documentation states: "show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook." A is partial, B and C are unrelated-only D meets the need per ADS SDK.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".
NEW QUESTION # 71
You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?
- A. Accumulated Local Effects
- B. Feature Permutation Importance Explanations
- C. Feature Dependence Explanations
- D. Local Interpretable Model-Agnostic Explanations
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an explanation technique for feature-target relationships with correlated features.
* Evaluate Options:
* A: Permutation-Breaks with high correlation.
* B: LIME-Local, not global relationships.
* C: Dependence-Not a standard term; vague.
* D: ALE-Handles correlation, shows feature effects-correct.
* Reasoning: ALE is robust to correlated features, ideal here.
* Conclusion: D is correct.
OCI documentation states: "Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation." B is local, C isn't defined-only D fits per OCI's explanation tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".
NEW QUESTION # 72
Which statement is true about standards?
- A. They are methods and instructions on how to maintain or accomplish the directives of the policy
- B. They may be audited
- C. They are the foundation of corporate governance
- D. They are the result of a regulation or contractual requirement or an industry requirement
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a true statement about standards in an OCI context (likely governance/security).
* Understand Standards: Rules or benchmarks, often compliance-related.
* Evaluate Options:
* A: Auditable-True; standards are checked for adherence.
* B: Result of requirements-Partially true, but not always.
* C: Methods/instructions-More procedural, not defining standards.
* D: Foundation of governance-Broad, not specific to standards.
* Reasoning: A is universally true-standards face audits (e.g., SOC, ISO).
* Conclusion: A is correct.
OCI documentation notes: "Standards (e.g., security standards) may be audited (A) to ensure compliance with OCI policies or external regulations." B is a source, C describes procedures, D is too vague-only A is consistently true per OCI's compliance framework.
Oracle Cloud Infrastructure Security Documentation, "Compliance and Standards".
NEW QUESTION # 73
You have configured the Management Agent on an Oracle Cloud Infrastructure (OCI) Linux instance for log ingestion purposes. Which is a required configuration for OCI Logging Analytics service to collect data from multiple logs of this instance?
- A. Log - Log Group Association
- B. Log Group - Source Association
- C. Entity - Log Association
- D. Source - Entity Association
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the required configuration for OCI Logging Analytics to collect logs from an instance.
* Understand Logging Analytics: Collects and analyzes logs from OCI resources via Management Agents.
* Key Concepts:
* Entity: Represents the instance (e.g., Linux VM).
* Source: Defines log locations (e.g., file paths).
* Log Group: Organizes logs for analysis.
* Evaluate Options:
* A: Log-Log Group-Groups logs, not collection setup.
* B: Entity-Log-Links instance to logs, but not source-specific.
* C: Source-Entity-Maps log sources to the instance-correct.
* D: Log Group-Source-Post-collection organization, not ingestion.
* Reasoning: C establishes the link between the instance and its log sources-key for ingestion.
* Conclusion: C is correct.
OCI documentation states: "To collect logs using Logging Analytics, configure a Source-Entity Association (C) to link the Management Agent on the instance (entity) to specific log sources (e.g., file paths)." A and D organize logs post-collection, B is less specific-only C is required for ingestion per OCI's Logging Analytics setup.
Oracle Cloud Infrastructure Logging Analytics Documentation, "Configuring Log Collection".
NEW QUESTION # 74
You have trained three different models on your dataset using Oracle AutoML. You want to visualize the behavior of each of the models, including the baseline model, on the test set. Which class should be used from the Accelerated Data Science (ADS) SDK to visually compare the models?
- A. EvaluationMetrics
- B. ADSExplainer
- C. ADSEvaluator
- D. ADSTuner
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the ADS SDK class for visualizing model performance comparison.
* Understand ADS Classes: Each serves a specific ML purpose-visualization requires evaluation tools.
* Evaluate Options:
* A. EvaluationMetrics: Likely a typo-meant EvaluationsMetrics? Not a standalone class for visualization.
* B. ADSEvaluator: Designed to evaluate and visualize model performance (e.g., ROC curves)- correct.
* C. ADSExplainer: Explains model predictions (e.g., SHAP), not comparative visualization.
* D. ADSTuner: Tunes hyperparameters, not for visualization.
* Reasoning: ADSEvaluator provides comparative plots (e.g., precision-recall) for multiple models, including baselines.
* Conclusion: B is correct.
OCI documentation states: "The ADSEvaluator class in ADS SDK (B) enables visualization of model performance metrics, such as ROC curves and confusion matrices, for multiple models on a test set, including baselines." EvaluationMetrics (A) isn't a class, ADSExplainer (C) focuses on interpretability, and ADSTuner (D) is for tuning-only B fits the visualization need per OCI's ADS toolkit.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSEvaluator Class".
NEW QUESTION # 75
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. 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. 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
- D. 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
Answer: B
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 # 76
Which Web Application Firewall (WAF) service component must be configured to allow, block, or log network requests when they meet specified criteria?
- A. Protection rules
- B. Bot Management
- C. Web Application Firewall policy
- D. Origin
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the WAF component that controls request actions based on criteria.
* Understand WAF Components:
* Protection Rules: Define conditions and actions (e.g., allow, block, log).
* Bot Management: Handles bot traffic, not general request rules.
* Origin: Backend server endpoint, not rule-based.
* WAF Policy: Umbrella config, but rules specify actions.
* Evaluate Options:
* A: Protection rules-Set specific criteria and actions-correct.
* B: Bot Management-Bot-specific, not general requests.
* C: Origin-Defines source, not actions.
* D: WAF policy-Broad config, not the granular rules.
* Reasoning: Protection rules directly manage request behavior-fit the requirement.
* Conclusion: A is correct.
OCI documentation states: "Protection rules (A) in WAF define conditions (e.g., IP, URL) and actions (allow, block, log) for incoming requests." Bot Management (B) targets bots, Origin (C) is a target server, and WAF Policy (D) encompasses rules but isn't the action specifier-only A aligns with OCI's WAF configuration.
Oracle Cloud Infrastructure WAF Documentation, "Protection Rules".
NEW QUESTION # 77
......
Latest 100% Passing Guarantee - Brilliant 1z0-1110-25 Exam Questions PDF: https://www.realexamfree.com/1z0-1110-25-real-exam-dumps.html
1z0-1110-25 Exam Dumps - Try Best 1z0-1110-25 Exam Questions: https://drive.google.com/open?id=1hcoHy97Felcl-bWfAP2e6dGqUoYB8-M8
