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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. You are monitoring a GPU-accelerated ETL pipeline using RAPIDS cuDF and Dask-cuDF. You suspect that a bottleneck is causing the pipeline to slow down.
Which of the following methods is the most effective way to diagnose performance bottlenecks in your data processing pipeline?

A) Use print() statements in the code to manually track execution times of different operation
B) Increase the batch size of data loading without checking GPU memory usage
C) Monitor CPU usage in the system to detect high CPU load that might indicate a bottleneck
D) Use NVIDIA Nsight Systems to profile GPU utilization and identify potential kernel execution inefficiencies

3. Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

A) Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.
B) Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
C) Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.
D) Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.

4. You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?

A) Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
B) Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.
C) Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
D) Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.

5. A data engineer is using cuDF in NVIDIA RAPIDS to generate a large synthetic dataset for machine learning training. The dataset consists of numerical and categorical features. The engineer needs to generate millions of rows efficiently while preserving the relationships between features.
Which of the following approaches is the most optimal?

A) Use cudf.to_pandas(), generate synthetic data using pandas and Scikit-learn, and then convert it back to cuDF.
B) Leverage cuML's PCA.inverse_transform() after fitting PCA to the original dataset to generate new synthetic samples.
C) Use cudf.Series.random() to create independent random values for each column separately.
D) Train a cuML KMeans model on the original data and use the cluster centroids as new synthetic data points.

Solutions: