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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning Fundamentals | 20% | - Unsupervised learning
|
| MLOps and OCI Integration | 20% | - Automation and pipelines
|
| OCI Data Science Service | 30% | - Projects and notebooks
|
| Model Development and Deployment | 30% | - Model deployment
|
>> New 1Z0-1110-26 Dumps Sheet <<
A lot of progress is being made in the Oracle sector today. Many companies offer job opportunities to qualified candidates, but they have specific 1Z0-1110-26 certification criteria to select qualified candidates. Thus, they can filter out effective and qualified candidates from the population. Oracle Cloud Infrastructure Data Science Professional (1Z0-1110-26) must be taken and passed to become a certified individual.
NEW QUESTION # 136
Which Web Application Firewall (WAF) service component must be configured to allow, block, or log network requests when they meet specified criteria?
Answer: D
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.
1: Oracle Cloud Infrastructure WAF Documentation, "Protection Rules".
NEW QUESTION # 137
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 dat a. Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?
Answer: D
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.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".
NEW QUESTION # 138
You loaded data into Oracle Cloud Infrastructure (OCI) Data Science. To transform the data, you want to use the Accelerated Data Science (ADS) SDK. When you applied the get_recommendations() tool to the ADSDataset object, it showed you user-detected issues with all the recommended changes to apply to the dataset. Which option should you use to apply all the recommended transformations at once?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Apply all recommended transformations from get_recommendations() in ADS.
Understand ADS Tools: get_recommendations() suggests fixes (e.g., missing values).
Evaluate Options:
A: Returns transformed data—Not for applying—incorrect.
B: Sklearn-style, not ADS-specific—incorrect.
C: auto_transform()—Applies all recommendations—correct.
D: Visualizes, doesn’t apply—incorrect.
Reasoning: auto_transform() executes the fixes suggested by get_recommendations().
Conclusion: C is correct.
OCI documentation states: “After get_recommendations() identifies issues, use auto_transform() (C) on the ADSDataset to apply all recommended transformations at once.” A retrieves, B is external, D visualizes—only C aligns with OCI’s ADS transformation workflow.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Data Transformation Methods".
NEW QUESTION # 139
You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces could you use to save the model artifact into the model catalog?
Answer: A,B,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify interfaces to save a 2 GB model to the Model Catalog.
Evaluate Options:
A: OCI CLI—Supports Data Science tasks—possible but not primary.
B: ADS SDK—Designed for model catalog ops—correct.
C: ODSC CLI—Not standard; likely typo for OCI CLI.
D: Console—GUI for catalog uploads—correct.
E: OCI Python SDK—Programmatic catalog access—correct.
F: Git CLI—Version control, not catalog-related.
Reasoning: B, D, E are OCI’s primary interfaces; A is valid but less emphasized.
Conclusion: B, D, E are correct (A plausible but not top-tier).
OCI documentation lists “ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog.” OCI CLI (A) works but isn’t highlighted, C isn’t real, and F is unrelated—B, D, E are the standard trio.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".
NEW QUESTION # 140
Which function's objective is to represent the difference between the predictive value and the target value?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the function that measures the difference between predicted and actual values in machine learning.
Understand ML Functions:
Optimizer function: Adjusts model parameters to minimize error (e.g., gradient descent)—it uses the cost, not defines it.
Fit function: Trains the model by fitting it to data—process-oriented, not a measure.
Update function: Typically updates weights during training—not a standard term for error measurement.
Cost function: Quantifies prediction error (e.g., MSE, cross-entropy)—directly represents the difference.
Evaluate Options:
A: Optimizer minimizes the cost, not the cost itself—incorrect.
B: Fit executes training, not error definition—incorrect.
C: Update is vague and not a standard ML term for this—incorrect.
D: Cost function (e.g., loss) measures prediction vs. target—correct.
Reasoning: The cost function (or loss function) is the mathematical representation of error, guiding optimization.
Conclusion: D is the correct answer.
In OCI Data Science, the documentation explains: “The cost function (or loss function) measures the difference between the model’s predicted values and the actual target values, such as mean squared error for regression or cross-entropy for classification.” Optimizers (A) use this to adjust weights, fit (B) is a training step, and update (C) isn’t a defined function here—only the cost function (D) fits the description. This aligns with standard ML terminology and OCI’s AutoML processes.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Concepts - Cost Functions".
NEW QUESTION # 141
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