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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with optimizing an ETL pipeline that processes petabytes of data daily. Your organization is already using Apache Spark for distributed data processing but is experiencing performance bottlenecks. You need a solution that improves execution speed without requiring extensive code modifications.
Which of the following solutions best meets your needs?

A) Switching to Dask and RAPIDS to fully utilize GPU acceleration
B) Rewriting the entire ETL pipeline in CUDA to maximize GPU efficiency
C) Using Apache Arrow to optimize in-memory columnar processing within Spark
D) Using the NVIDIA Spark RAPIDS Accelerator to run Spark workloads on GPUs

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

3. You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?

A) Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.
B) Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.
C) Use cuDF to manipulate data distributions and generate new data points based on existing features.

4. You are tasked with processing a large dataset of 100 million records for a deep learning project using NVIDIA technologies. You need to determine the most efficient data processing library for this task to maximize performance and reduce processing time.
Which of the following libraries is best suited for this task?

A) pandas
B) Dask
C) cuDF
D) PySpark

5. You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?

A) Split the dataset into multiple files based on a logical partitioning key, such as a date column.
B) Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
C) Store the entire dataset in a single large file to minimize the number of files in the directory.
D) Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.

Solutions: