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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

A) Use int8 for categorical features where there are fewer than 256 categories.
B) Use datetime64[ns] for timestamp features to ensure high precision.
C) Use int32 for all numerical features to save memory.
D) Use object data types for categorical features to avoid type conversion overhead.
E) Use float64 for all numerical features to ensure maximum precision.

2. You are working with a large dataset containing numeric and categorical features, which will be processed using NVIDIA RAPIDS cuDF for accelerated analytics.
To optimize performance while minimizing memory usage, which data type is the most appropriate for storing a categorical variable with a small number of unique values?

A) category - Optimizes storage and computation for categorical data in cuDF.
B) int64 - Provides high precision and avoids potential overflow.
C) bool - Minimizes memory usage and supports efficient operations for categorical data.
D) float32 - Reduces memory consumption compared to float64 while maintaining precision.

3. You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

A) Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
B) Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
C) Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
D) Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution

4. A data science team is deploying a deep learning model for real-time inference. The model is optimized for inference on an NVIDIA A100 GPU, but the team notices that inference latency is higher than expected.
Which of the following optimizations is most effective in reducing inference latency?

A) Enable CPU offloading to balance the workload between the CPU and GPU.
B) Use mixed-precision inference with TensorRT to accelerate computation.
C) Increase the batch size significantly to improve GPU utilization.
D) Reduce the model size by randomly pruning neurons without retraining.

5. A team of data engineers is working on an Apache Spark-based distributed computing pipeline that leverages NVIDIA GPUs and RAPIDS. They notice that shuffle operations are causing significant slowdowns in performance.
Which optimization strategy should they implement to reduce shuffle impact?

A) Use Spark's default shuffle partitioning without any modification, as GPUs inherently optimize shuffle operations.
B) Use RAPIDS Spark-RAPIDS Plugin with GPU-accelerated caching to minimize redundant shuffle operations.
C) Store shuffle data in Apache Parquet format on disk for faster access and reduced memory overhead.
D) Disable GPU memory caching to allow automatic CPU-based shuffle optimization.

Solutions: