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You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?

• A. The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.
• B. The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.
• C. The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.
• D. The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.

A data scientist is working with an imbalanced dataset in a fraud detection project. The dataset contains 1 million transactions, but only 2% of them are labeled as fraudulent. To improve the performance of the model, the scientist decides to generate synthetic data using NVIDIA RAPIDS cuDF.
Which of the following approaches is the best way to generate synthetic samples while preserving data characteristics?

• A. Use cudf.DataFrame.interpolate(method='linear') to create new fraudulent samples by interpolating between existing ones.
• B. Use cudf.DataFrame.append(cudf.DataFrame.random()) to create new fraudulent transactions.
• C. Apply cuML.SMOTE() to generate synthetic samples based on the minority class distribution.
• D. Use cudf.DataFrame.sample(frac=0.5, replace=True) to oversample the minority class.

You are using RAPIDS and Dask-cuDF to process a large-scale ETL pipeline. The workflow involves multiple join and groupby operations, which are causing excessive shuffling.
How can you best optimize caching to reduce shuffle overhead?

• A. Force every operation to be recomputed from the raw dataset to ensure accurate results.
• B. Cache data using Apache Arrow's in-memory format, but process all operations on CPU.
• C. Use dask.persist() to store frequently accessed cuDF DataFrames in GPU memory, reducing recomputation and shuffle operations.
• D. Split the dataset into multiple smaller Pandas DataFrames and store them in memory for quick retrieval.

In the context of the CRISP-DM process, you are tasked with building a machine learning model on a large dataset. You decide to leverage GPU resources to accelerate training.
Which of the following steps in the CRISP-DM process are most likely to benefit from the use of GPU resources in the cloud environment? (Select two)

• A. Model Deployment and Monitoring
• B. Business Understanding
• C. Data Understanding
• D. Data Exploration
• E. Model Training and Evaluation

You need to process a dataset containing 10 billion rows, applying complex transformations and aggregations. The dataset does not fit into RAM, and you need an efficient, scalable solution for parallel processing.
Which of the following is the best choice?

• A. Dask
• B. Pandas
• C. Vaex
• D. PySpark