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NEW QUESTION # 30
What is a common maxim about data scientists?
- A. They spend 80% of their time finding and preparing data and 20% analyzing it.
- B. They spend 80% of their time on failed analytics projects and 20% doing useful work.
- C. They spend 80% of their time analyzing data and 20% finding and preparing it.
Answer: A
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 # 31
You are a data scientist leveraging Oracle Cloud Infrastructure (OCI) to create a model and need some additional Python libraries for processing genome sequencing dat a. 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 any open-source package available in a publicly accessible Python Package Index (PyPI) repository
- C. You cannot install a library that’s not preinstalled in the provided image
- D. You can install private or custom libraries from your own internal repositories
- 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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions".
NEW QUESTION # 32
Which step is a part of the AutoML pipeline?
- A. Model Deployment
- B. Feature Selection
- C. Feature Extraction
- D. Model saved to Model Catalog
Answer: B
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 # 33
You want to make your model more frugal to reduce the cost of collecting and processing dat a. 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. cramersv_plot()
- B. correlation_ratio_plot()
- C. pearson_plot()
- D. corr()
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.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".
NEW QUESTION # 34
You want to use ADSTuner to tune the hyperparameters of a supported model you recently trained. You have just started your search and want to reduce the computational cost as well as assess the quality of the model class that you are using. What is the most appropriate search space strategy to choose?
- A. Detailed
- B. ADSTuner doesn’t need a search space to tune the hyperparameters
- C. Pass a dictionary that defines a search space
- D. Perfunctory
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an ADSTuner strategy to minimize cost and assess model quality.
Understand ADSTuner: Optimizes hyperparameters with configurable search spaces.
Evaluate Options:
A: Detailed—Exhaustive, high cost—incorrect.
B: No search space—False; tuning requires a space.
C: Perfunctory—Quick, low-cost assessment—correct.
D: Dictionary—Defines space but not a strategy.
Reasoning: Perfunctory balances cost and initial quality check.
Conclusion: C is correct.
OCI documentation states: “ADSTuner’s perfunctory strategy (C) performs a quick, low-cost search to assess model quality, ideal for initial tuning.” Detailed (A) is costly, B misstates requirements, and D is a method, not a strategy—only C fits the goal.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "ADSTuner Search Strategies".
NEW QUESTION # 35
You want to create a user group for a team of external data science consultants. The consultants should only have the ability to see Data Science resource details but not have the ability to create, delete, or update Data Science resources. What verb should you write in the policy?
- A. Manage
- B. Inspect
- C. Read
- D. Use
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI IAM Policies: Policies control access using verbs like “inspect,” “read,” “use,” and “manage.” Define Requirement: Consultants need view-only access without modification rights.
Evaluate Verbs:
A . Use: Allows interaction (e.g., running jobs)—too permissive.
B . Inspect: Grants view-only access to resource details—matches requirement.
C . Manage: Full control (create, update, delete)—too permissive.
D . Read: Includes viewing content (e.g., data), not just metadata—slightly broader than needed.
Conclusion: “Inspect” (B) is the precise verb for view-only access to resource details.
In OCI Identity and Access Management (IAM), the “inspect” verb allows listing and viewing resource metadata without granting modification or data access rights, ideal for this scenario. This is confirmed in the IAM policy reference. (Oracle Cloud Infrastructure Documentation, "IAM Policy Verbs").
NEW QUESTION # 36
Which statement accurately describes an aspect of machine learning models?
- A. Model performance degrades over time due to changes in data.
- B. A high-quality model will not need to be retrained as new information is received.
- C. Data models are more static and generally require fewer updates than software code.
- D. Static predictions become increasingly accurate over time.
Answer: A
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Drift".
NEW QUESTION # 37
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image dat a. Which of the following THREE are possible ways to annotate an image in Data Labeling?
- A. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image
- B. Adding multiple labels to an image
- C. Adding a single label to an image
- D. Adding labels to an image using object detection, by drawing bounding boxes to an image
- E. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
Answer: B,C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three annotation methods in OCI Data Labeling for images.
Understand Data Labeling: Supports image annotations for ML.
Evaluate Options:
A: Semantic segmentation with boxes—Incorrect; segmentation is pixel-based, not boxes.
B: Single label (classification)—Supported—correct.
C: No bounding boxes—False; boxes are supported.
D: Object detection with boxes—Supported—correct.
E: Multiple labels (multi-label)—Supported—correct.
Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.
Conclusion: B, D, E are correct.
OCI documentation states: “Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E).” A misdefines segmentation, C contradicts support—only B, D, E are valid per OCI’s Data Labeling features.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".
NEW QUESTION # 38
You are a data scientist designing an air traffic control model, and you choose to leverage Oracle AutoML. You understand that the Oracle AutoML pipeline consists of multiple stages and automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML pipeline?
- A. Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning
- B. Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning
- C. Algorithm selection, Adaptive sampling, Feature selection, Hyperparameter tuning
- D. Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Sequence OCI AutoML pipeline stages.
Stages:
Adaptive sampling: Reduces data size if large.
Feature selection: Picks relevant features.
Algorithm selection: Chooses best model type.
Hyperparameter tuning: Optimizes model params.
Evaluate: C (sampling, features, algorithms, tuning) matches logical flow—data first, then model.
Reasoning: Sampling precedes feature work—standard in OCI.
Conclusion: C is correct.
OCI documentation states: “AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C).” Sampling reduces data first, then features and models are optimized—other orders (A, B, D) misalign with OCI’s sequence.
1: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".
NEW QUESTION # 39
You have just started as a data scientist at a healthcare company. You have been asked to analyze and improve a deep neural network model, which was built based on the electrocardiogram records of patients. There are no details about the model framework that was built. What would be the best way to find more details about the machine learning models inside the model catalog?
- A. Check for metadata tags
- B. Check for model taxonomy details
- C. Refer to the code inside the model
- D. Check for provenance details
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.
Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.
Evaluate Options:
A . Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn’t typically include readable source code; it’s a trained object, not the training script.
B . Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.
C . Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).
D . Check for provenance details: Provenance tracks the model’s creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.
Reasoning: Provenance details are designed to document the “how” and “what” of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.
Conclusion: D is the best approach for detailed investigation.
In OCI Data Science, the Model Catalog stores provenance information, which includes “details about the model’s origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references.” This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.
NEW QUESTION # 40
Which model has an open-source, open model format that allows you to run machine learning models on different platforms?
- A. ONNX
- B. PyTorch
- C. TensorFlow
- D. PySpark
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify an open model format for cross-platform ML model execution.
Evaluate Options:
A . PySpark: A big data framework, not a model format.
B . PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.
C . TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.
D . ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.
Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.
Conclusion: D is the correct choice.
ONNX (D) is “an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow,” per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats—only ONNX ensures cross-platform compatibility.
1: Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".
NEW QUESTION # 41
You have trained a binary classifier for a loan application and saved this model into the model catalog. A colleague wants to examine the model, and you need to share the model with your colleague. From the model catalog, which model artifacts can be shared?
- A. Metadata, hyperparameters, metrics only
- B. Models, model metadata, hyperparameters, metrics
- C. Models and metrics only
- D. Model metadata and hyperparameters only
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Model Catalog: The OCI Model Catalog stores trained models and associated details.
Identify Shareable Artifacts: When sharing, all components—model file, metadata (e.g., name, description), hyperparameters (e.g., learning rate), and metrics (e.g., accuracy)—are accessible.
Evaluate Options:
A: Excludes the model itself—incorrect.
B: Excludes metrics—incorrect.
C: Excludes metadata and hyperparameters—incorrect.
D: Includes all components—correct.
Conclusion: D is comprehensive and accurate.
The OCI Model Catalog allows sharing of the model artifact (the trained model), metadata, hyperparameters, and performance metrics, enabling full examination by colleagues. This isdetailed in the official documentation. (Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Overview").
NEW QUESTION # 42
Which is NOT a compliance document?
- A. Certificate
- B. Attestation
- C. Bridge letter
- D. Penetration test report
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a non-compliance document in OCI context.
Understand Compliance Docs: Formal attestations of adherence (e.g., SOC, ISO).
Evaluate Options:
A: Certificate—Proof of compliance (e.g., ISO)—compliance doc.
B: Pen test report—Security test result, not formal compliance—correct.
C: Attestation—Statement of compliance—compliance doc.
D: Bridge letter—Links audit periods—compliance doc.
Reasoning: B is operational, not a compliance artifact.
Conclusion: B is correct.
OCI documentation lists “compliance documents like certificates (A), attestations (C), and bridge letters (D) for standards like SOC or ISO; penetration test reports (B) are security assessments, not formal compliance docs.” Only B stands apart per OCI’s compliance terminology.
1: Oracle Cloud Infrastructure Compliance Documentation, "Compliance Artifacts".
NEW QUESTION # 43
Why is data sampling useful for data scientists?
- A. It enables them to use a representative subset of data to build accurate analytical models more quickly.
- B. It lets them analyze datasets in small batches to reduce their use of system resources.
- C. It reduces the amount of data storage space that’s required for data science applications.
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine the primary benefit of data sampling.
Define Sampling: Selecting a subset of data to represent the whole—used in ML/statistics.
Evaluate Options:
A: Small batches reduce resources—True but not the main purpose.
B: Reduces storage—Incidental, not the goal.
C: Representative subset for faster, accurate models—Core purpose of sampling.
Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).
Conclusion: C is correct.
OCI documentation states: “Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical.” A focuses on resources (secondary), B on storage (not primary)—only C captures the analytical intent per OCI’s AutoML sampling approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".
NEW QUESTION # 44
You want to build a multistep machine learning workflow by using the Oracle Cloud Infrastructure (OCI) Data Science Pipeline feature. How would you configure the conda environment to run a pipeline step?
- A. Use command-line variables
- B. Use environmental variables
- C. Configure a compute shape
- D. Configure a block volume
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Configure conda env for a pipeline step.
Evaluate Options:
A: Shape—Infra, not env config.
B: Volume—Storage, not env.
C: Command-line—Step args, not env.
D: Env variables—Sets conda path—correct.
Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).
Conclusion: D is correct.
OCI documentation states: “Configure a pipeline step’s conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition.” A, B, and C address other aspects—only D fits env config.
1: Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".
NEW QUESTION # 45
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