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| Section | Weight | Objectives |
|---|---|---|
| Implement End-to-End Machine Learning Lifecycle | 45% | - Deploy models and manage endpoints - Model saving, cataloging, and versioning - Data preparation, exploration, and transformation - Model development, training, and evaluation - Use AutoML and built-in algorithms |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science - Capabilities of the Accelerated Data Science (ADS) SDK |
| Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking - Governance, auditing, and compliance |
| Design and Set Up Data Science Workspace | 15% | - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions - Manage access control, security, and IAM integration |
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NEW QUESTION # 15
What is a common maxim about data scientists?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a widely accepted maxim about data scientists’ time allocation.
Understand Data Science Workflow: Involves data collection, preparation, and analysis—time distribution is key.
Evaluate Options:
A: 80% on finding/preparing, 20% analyzing—Reflects the data wrangling challenge.
B: 80% analyzing, 20% finding/preparing—Inverts the common perception.
C: 80% on failed projects, 20% useful—Pessimistic, not a standard maxim.
Reasoning: Industry consensus (e.g., “80/20 rule”) emphasizes data prep as the bulk of effort due to messy real-world data.
Conclusion: A is correct.
OCI Data Science documentation aligns with industry norms: “Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data.” B reverses this, and C isn’t supported—only A reflects this widely cited maxim from sources like Forbes and OCI’s practical guidance.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview".
NEW QUESTION # 16
What is the name of the machine learning library used in Apache Spark?
Answer: D
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 # 17
Which step is a part of the AutoML pipeline?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a step in OCI’s AutoML pipeline.
Understand AutoML: Automates model building—includes preprocessing, selection, and tuning.
Evaluate Options:
A: Feature Extraction (e.g., PCA) isn’t explicitly part of OCI AutoML—too specific.
B: Saving to Model Catalog is post-AutoML, not a pipeline step.
C: Deployment is a separate action after AutoML—incorrect.
D: Feature Selection (e.g., choosing relevant features) is a core AutoML step—correct.
Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning—D fits.
Conclusion: D is correct.
OCI AutoML’s pipeline includes “feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning,” per the documentation. Extraction (A) isn’t highlighted, while saving (B) and deployment (C) are post-process actions—only Feature Selection (D) is an integral automated step.
1: Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".
NEW QUESTION # 18
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 dat a. Select the correct sequence of steps to predict the revenue demand values for the upcoming quarter.
Answer: B
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. (Oracle Cloud Infrastructure Data Science Documentation, "Time Series Forecasting Workflow").
NEW QUESTION # 19
You are a data scientist leveraging the Oracle Cloud Infrastructure (OCI) Language AI service for various types of text analyses. Which TWO capabilities can you utilize with this tool?
Answer: C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify two OCI Language AI capabilities.
Understand OCI Language: Focuses on text analysis tasks.
Evaluate Options:
A: Table extraction—Vision, not Language—incorrect.
B: Punctuation correction—Not offered—incorrect.
C: Sentence diagramming—Not supported—incorrect.
D: Topic classification—Supported (custom/pretrained)—correct.
E: Sentiment analysis—Supported (pretrained)—correct.
Reasoning: D and E are core text analysis features of OCI Language.
Conclusion: D and E are correct.
OCI documentation states: “OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features.” A belongs to Vision, B and C aren’t available—only D and E match OCI Language’s capabilities.
1: Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".
NEW QUESTION # 20
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