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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A) Efficient handling of matrix operations in machine learning models
B) Better for memory-intensive workloads
C) Lower cost of cloud infrastructure
D) Lower energy consumption compared to CPUs
E) Faster parallel processing for large datasets

2. You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

A) df = cudf.read_parquet("data.parquet")
B) df = cudf.to_gpu(pd.read_parquet("data.parquet"))
C) df = cudf.read_csv("data.parquet")
D) df = pd.read_parquet("data.parquet")

3. Which of the following best describes a key advantage of using cloud-based GPU instances for machine learning model training?

A) Cloud GPU instances cannot support containerized workloads, limiting their applicability for MLOps and CI/CD pipelines.
B) Cloud GPUs provide dynamically scalable resources, allowing users to increase or decrease compute power based on demand without upfront hardware investment.
C) Cloud-based GPU instances offer lower latency and better network performance compared to on- premise deployments, regardless of geographical location.
D) Cloud GPUs are always more cost-effective than on-premise GPUs, as they do not incur long-term usage costs.

4. A data scientist is working with a large dataset that contains string-based numeric values that need to be converted to floating-point numbers for further analysis. The dataset is stored as a cuDF DataFrame, and the scientist needs to ensure the conversion is performed optimally on a GPU.
Which of the following is the best method for converting string-based numeric values to floating-point numbers using NVIDIA-accelerated processing?

A) Use NumPy's astype(float) method after converting the cuDF DataFrame into a NumPy array.
B) Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.
C) Use pandas.to_numeric() since pandas automatically handles type conversion.
D) Use cudf.DataFrame.astype(float) to convert string values to floating-point numbers efficiently on a GPU.

5. You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
Which of the following is the correct and most efficient way to perform this conversion in cuDF?

A) df['sales'] = df['sales'].to_numeric('int32')
B) df['sales'].convert_dtypes('int32')
C) df['sales'].apply(lambda x: int(x))
D) df['sales'] = df['sales'].astype('int32')

Solutions: