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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning Fundamentals | 20% | - Unsupervised learning
|
| Model Development and Deployment | 30% | - Model training
|
| MLOps and OCI Integration | 20% | - OCI ecosystem
|
| OCI Data Science Service | 30% | - Projects and notebooks
|
>> 1Z0-1110-26 Certified Questions <<
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NEW QUESTION # 153
You have a dataset with fewer than 1000 observations, and you are using Oracle AutoML to build a classifier. While visualizing the results of each stage of the Oracle AutoML pipeline, you notice that no visualization has been generated for one of the stages. Which stage is not visualized?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the non-visualized AutoML stage with small data.
Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.
Evaluate Options:
A: Feature selection—Visualized (e.g., feature importance).
B: Algorithm selection—Visualized (e.g., algorithm scores).
C: Adaptive sampling—Skipped/visualization absent for <1000 rows.
D: Hyperparameter tuning—Visualized (e.g., trial plots).
Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.
Conclusion: C is correct.
OCI AutoML documentation notes: “Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it’s skipped, and no visualization is generated.” Other stages (A, B,D) produce visuals—only C is absent here.
1: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".
NEW QUESTION # 154
True or false? Bias is a common problem in data science applications.
Answer: B
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".
NEW QUESTION # 155
Which function's objective is to represent the difference between the predictive value and the target value?
Answer: B
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 # 156
You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?
Answer: B,C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three required params for an OCI Data Flow app config.
Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.
Evaluate Options:
A: Archive path—Optional if script is in Object Storage—incorrect.
B: Local script path—Not needed; script is uploaded—incorrect.
C: Compartment—Required for resource scope—correct.
D: Bucket—Required for script storage/access—correct.
E: Display name—Required for app identification—correct.
Reasoning: C, D, E are mandatory metadata for Data Flow creation—script location is specified via bucket.
Conclusion: C, D, E are correct.
OCI documentation states: “To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object.” Local paths (B) or archives (A) are optional or handled separately—only C, D, E are required per OCI’s Data Flow API spec.
1: Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".
NEW QUESTION # 157
Which Web Application Firewall (WAF) service component must be configured to allow, block, or log network requests when they meet specified criteria?
Answer: C
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 # 158
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