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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large dataset in a GPU-accelerated environment, and one of the columns, revenue, contains numeric values representing the annual revenue for companies. The revenue values are in the billions of dollars.
Which of the following is the most memory-efficient data type for the revenue column in a cuDF DataFrame?

A) df['revenue'] = df['revenue'].astype('float64')
B) df['revenue'] = df['revenue'].astype('uint32')
C) df['revenue'] = df['revenue'].astype('float32')
D) df['revenue'] = df['revenue'].astype('int64')

2. You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

A) Acquiring and exploring the dataset to assess quality, completeness, and potential biases.
B) Identifying and preparing feature engineering strategies to improve model accuracy.
C) Selecting the most appropriate machine learning algorithm for the prediction task.
D) Deploying the model into a production environment for real-time inference.

3. A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

A) Perform all analysis on the CPU to avoid potential GPU memory limitations.
B) Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.
C) Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
D) Downsample the dataset and analyze a subset using Pandas for efficiency.

4. You are working with a dataset containing 2 billion rows of financial transactions, and you need to perform exploratory data analysis (EDA) before building a predictive model.
Which of the following approaches is the most appropriate for handling this data efficiently?

A) Use SQLite to store the data locally and run queries sequentially to minimize memory consumption.
B) Convert the dataset to JSON format and use Python's built-in json module to parse and analyze it efficiently.
C) Use an accelerated data science framework such as RAPIDS cuDF or Dask to distribute computations across GPUs.
D) Load the entire dataset into a Pandas DataFrame and analyze it using Pandas built-in methods.

5. You are working with a data science project that requires GPU acceleration for machine learning tasks. Your team is facing challenges with software version conflicts between different dependencies when deploying the project on different systems.
Which of the following solutions should you consider to efficiently manage software dependencies and avoid conflicts? (Select two)

A) Manually install all dependencies directly on the host machine to avoid using dependency management tools.
B) Set up a virtual machine for each different dependency configuration to isolate environments.
C) Use Docker to containerize the project, ensuring that the dependencies and environment are consistent across different systems.
D) Install GPU drivers on the host machine and rely on the local system environment for dependency management.
E) Use Conda to create isolated environments for different versions of dependencies, ensuring version compatibility.

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