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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric features are standardized for better model performance.
Which of the following is the best approach for scaling data using RAPIDS?

A) df_scaled = (df - df.min()) / (df.max() - df.min())
B) df_scaled = scaler.fit_transform(df)
C) df_scaled = df.apply(lambda x: x / np.linalg.norm(x))
D) df_scaled = df / df.max()
E) scaler = cuml.preprocessing.StandardScaler()

2. 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) Store shuffle data in Apache Parquet format on disk for faster access and reduced memory overhead.
C) Use RAPIDS Spark-RAPIDS Plugin with GPU-accelerated caching to minimize redundant shuffle operations.
D) Disable GPU memory caching to allow automatic CPU-based shuffle optimization.

3. Which of the following Nvidia technologies is primarily used for performing benchmarking and optimizing GPU-accelerated deep learning workflows, especially focusing on model training performance?

A) Nvidia CUDA
B) Nvidia Triton Inference Server
C) Nvidia Nsight Systems
D) Nvidia DeepStream SDK

4. A research team is analyzing large-scale social interactions and wants to identify strongly connected communities within a massive graph dataset using NVIDIA's cuGraph library.
Which method would be the most efficient for this task?

A) Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.
B) Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.
C) Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.
D) Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.

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

Solutions: