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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A) Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing
B) Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations
C) Run the computation on a single GPU and let Dask handle CPU parallelism automatically
D) Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
E) Use Dask's default scheduler to distribute work across GPUs without any specific configuration

2. You need to deploy a machine learning model on a GPU-equipped system. The GPU has 16GB of VRAM, and the model requires approximately 12GB of memory during inference. However, additional system processes and other applications consume 5GB of VRAM.
What would happen if you attempt to run inference without making any optimizations, and how should you resolve the issue?

A) The model will run without issues because 16GB of VRAM is sufficient for a 12GB model
B) The model will run successfully but with reduced performance due to memory fragmentation
C) Switching from a GPU to CPU inference will resolve memory issues without performance loss
D) The model will fail to run due to out-of-memory (OOM) errors, and using a smaller batch size can help reduce memory usage

3. You have a multi-GPU cluster running RAPIDS with Dask to process a large dataset stored in Apache Parquet format. During execution, you notice some GPUs are underutilized, while others are overloaded, leading to uneven processing times.
What is the most effective way to balance the workload across GPUs?

A) Switch to a CPU-based framework like Spark to distribute the load evenly
B) Split the dataset into smaller chunks manually and assign them to GPUs
C) Increase the number of worker threads per GPU manually
D) Use Dask's adaptive scaling to dynamically adjust the number of GPU workers

4. A data engineer is preparing a dataset for training a deep learning model. The dataset contains numerical features with missing values, outliers, and inconsistent units.
Which of the following strategies is the most appropriate for ensuring a standardized and clean dataset?

A) Replace missing values with the mean, apply z-score normalization, and clip extreme outliers based on a threshold (e.g., 3 standard deviations).
B) Standardize the dataset using the mean and standard deviation, but keep missing values and outliers unchanged to avoid data manipulation.
C) Remove all rows with missing values and outliers to ensure only clean data is used.
D) Use the median to fill missing values, convert all numerical values into categorical bins, and apply Min-Max scaling.

5. 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) Use dask_gpu instead of dask_cuda to manage memory automatically across GPUs.
C) Ensure that each GPU's memory usage is manually monitored and adjusted, as Dask does not manage memory allocation automatically across GPUs.
D) Allow Dask to allocate data chunks dynamically without partitioning the dataset first, letting the system handle memory distribution automatically.

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