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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q158-Q163):

NEW QUESTION # 158
You are working as a data scientist for a healthcare company. They decide to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended stepsto develop a PySpark application in Oracle Cloud Infrastructure (OCI) Data Science?

A. Install a Spark conda environment, configure core-site.xml, launch a notebook session, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application
B. Launch a notebook session, configure core-site.xml, install a PySpark conda environment, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
C. Launch a notebook session, install a PySpark conda environment, configure core-site.xml, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
D. Configure core-site.xml, install a PySpark conda environment, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application, launch a notebook session

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence steps for PySpark app development.
* Steps:
* Launch notebook: First-sets up environment.
* Install PySpark conda: Second-adds Spark libraries.
* Configure core-site.xml: Third-connects to data.
* Develop app: Fourth-writes code.
* Data Flow: Fifth-scales (optional).
* Evaluate: D (1, 2, 3, 4, 5) matches this logical order.
* Reasoning: Notebook first, then setup and coding.
* Conclusion: D is correct.
OCI documentation states: "1) Launch a notebook session, 2) install a PySpark conda env, 3) configure core- site.xml, 4) develop your PySpark app, 5) optionally use Data Flow (D)." Other orders (A, B, C) misplace notebook launch or config-D is correct.
Oracle Cloud Infrastructure Data Science Documentation, "PySpark Development".

NEW QUESTION # 159
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 provenance details
B. Refer to the code inside the model
C. Check for metadata tags
D. Check for model taxonomy details

Answer: A

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.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.

NEW QUESTION # 160
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. Configure a compute shape
B. Configure a block volume
C. Use environmental variables
D. Use command-line variables

Answer: C

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.
Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".

NEW QUESTION # 161
You want to make your model more frugal to reduce the cost of collecting and processing data. 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. corr()
B. pearson_plot()
C. correlation_ratio_plot()
D. cramersv_plot()

Answer: C

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.
Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".

NEW QUESTION # 162
You have created a conda environment in your notebook session. This is the first time you are working with published conda environments. You have also created an Object Storage bucket with permission to manage the bucket. Which TWO commands are required to publish the conda environment?

A. conda activate /home/datascience/conda/<SLUG>
B. odsc conda list --override
C. odsc conda create --file manifest.yaml
D. odsc conda init --bucket_namespace <NAMESPACE> --bucket_name <BUCKET>
E. odsc conda publish --slug <SLUG>

Answer: D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Publish a conda env to Object Storage.
* Process: Initialize bucket config, then publish env.
* Evaluate Options:
* A: Publishes env with slug-correct final step.
* B: Lists envs-unrelated to publishing.
* C: Sets bucket details-required setup-correct.
* D: Creates env-not publishing.
* E: Activates env-not for sharing.
* Reasoning: C sets up, A executes-standard workflow.
* Conclusion: A and C are correct.
OCI documentation states: "To publish a conda environment, first run odsc conda init (C) with bucket namespace and name, then odsc conda publish (A) with a slug to upload to Object Storage." B, D, and E serve other purposes-only A and C are required per OCI's process.
Oracle Cloud Infrastructure Data Science CLI Reference, "Publishing Conda Environments".

NEW QUESTION # 163
......

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