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| Section | Objectives |
|---|---|
| Use Related OCI Services | - Design machine learning solutions for business use cases - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing |
| Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models - Prepare and manage datasets - Automate machine learning workflows and pipelines - Save and manage models using Model Catalog |
| OCI Data Science - Introduction and Configuration | - Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture |
| Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources - Create and configure Data Science projects |
| Apply MLOps Practices | - Implement model lifecycle management - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions |
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NEW QUESTION # 43
What is the name of the machine learning library used in Apache Spark?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify Apache Spark’s ML library.
Understand Spark: A big data framework with specialized libraries.
Evaluate Options:
A: MLib (correctly MLlib)—Spark’s machine learning library.
B: GraphX—Graph processing, not ML.
C: Structured Streaming—Streaming data, not ML.
D: HadoopML—Not a Spark library (Hadoop-related).
Reasoning: MLlib is Spark’s official ML toolkit (e.g., regression, clustering).
Conclusion: A is correct (noting “MLib” should be “MLlib”).
OCI Data Science supports Spark via Data Flow, where “MLlib (Machine Learning library) provides scalable ML algorithms.” GraphX (B) and Structured Streaming (C) serve other purposes, and HadoopML (D) isn’t real—MLlib (A) is the standard, despite the typo.
1: Oracle Cloud Infrastructure Data Flow Documentation, "Apache Spark MLlib".
NEW QUESTION # 44
What is feature engineering in machine learning used for?
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 1with C. Other options (A, B, D) relate to different ML stages.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".
NEW QUESTION # 45
Where do calls to stdout and stderr from score.py go in the model deployment?
Answer: C
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 # 46
The Oracle AutoML pipeline automates hyperparameter tuning by training the model with different parameters in parallel. You have created an instance of Oracle AutoML as oracle_automl and now you want an output with all the different trials performed by Oracle AutoML. Which of the following commands gives you the results of all trials?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Get all AutoML trial results.
Understand AutoML: Trials include hyperparameter tuning outcomes.
Evaluate Options:
A: print_trials()—Displays all trial results—correct.
B: visualize_tuning_trials()—Visualizes tuning, not full list.
C: visualize_adaptive_sampling_trials()—Specific to sampling, not all trials.
D: visualize_algorithm_selection_trials()—Specific to algorithms, not all.
Reasoning: A provides comprehensive trial output.
Conclusion: A is correct.
OCI AutoML documentation states: “print_trials() outputs a table of all trials performed, including hyperparameters and scores.” Visualization methods (B, C, D) focus on specific aspects—only A gives the full list.
1: Oracle Cloud Infrastructure AutoML Documentation, "Trial Output Methods".
NEW QUESTION # 47
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?
Answer: C
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Load Balancing".
NEW QUESTION # 48
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