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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with structured tabular data in a cloud-based GPU environment.
Your dataset contains the following columns:
Column Name Example Values Data Type Needed
user_id 15432, 98765, 43210 Integer
purchase_amt 12.99, 35.50, 100.75 Floating Point
category 'Books', 'Electronics' Categorical
Which of the following is the most optimal approach to assign data types to these columns to ensure efficient memory usage and computational performance?

A) 1. df['user_id'] = df['user_id'].astype('int16')
2. df['purchase_amt'] = df['purchase_amt'].astype('float16')
3. df['category'] = df['category'].astype('string')
B) 1. df['user_id'] = df['user_id'].astype('float32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
C) 1. df['user_id'] = df['user_id'].astype('int64')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
D) 1. df['user_id'] = df['user_id'].astype('int32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float32')
3. df['category'] = df['category'].astype('category')

2. Which of the following statements best describes the role of GPUs in accelerating data science workloads?

A) GPUs are designed primarily for rendering graphics and have limited utility in machine learning and deep learning applications.
B) GPUs are optimized for sequential data processing tasks, making them more efficient than CPUs for database operations.
C) GPUs use thousands of smaller cores that can execute many parallel computations simultaneously, making them ideal for large-scale matrix operations.
D) GPUs are only effective for acceleration when used in conjunction with Tensor Processing Units (TPUs), as they cannot train deep learning models independently.

3. When scaling data parallelism using Dask with multiple Nvidia GPUs, what is the key consideration to avoid memory issues when distributing large datasets?

A) Split the dataset into smaller partitions that fit into each GPU's memory to prevent out-of-memory errors, and let Dask manage data distribution.
B) Allow Dask to allocate data chunks dynamically without partitioning the dataset first, letting the system handle memory distribution automatically.
C) Use dask_gpu instead of dask_cuda to manage memory automatically across GPUs.
D) Ensure that each GPU's memory usage is manually monitored and adjusted, as Dask does not manage memory allocation automatically across GPUs.

4. You are processing a large-scale transportation network graph using NVIDIA cuGraph. The graph is extremely large, consuming almost all available GPU memory. Performance is deteriorating, and some computations fail due to memory exhaustion.
What is the best approach to efficiently handle this large graph while keeping computations on the GPU?

A) Convert the graph into a NetworkX graph and process it on the CPU to reduce GPU memory usage.
B) Use cuGraph's multi-GPU support via Dask-cuGraph to distribute the graph across multiple GPUs.
C) Manually split the graph into chunks and process each chunk separately without any coordination.
D) Store the graph as a large Python dictionary and use cuGraph only for specific queries.

5. A data scientist is working with datasets ranging from hundreds of megabytes to several terabytes and needs to select the most efficient NVIDIA-accelerated data processing library for optimal memory management and performance.
Which approach is best for selecting the appropriate library for different dataset sizes?

A) Use RAPIDS cuML for handling all data processing tasks, regardless of dataset size.
B) Use Pandas for all dataset sizes since Pandas has built-in multi-threading optimizations.
C) Always use Dask regardless of dataset size since Dask automatically scales from small to large datasets.
D) Use cuDF for small datasets and Dask-cuDF for larger datasets that do not fit in a single GPU's memory.

Solutions: