NVIDIA-Certified-Professional Accelerated Data Science : NCP-ADS

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

A) Use NumPy's statistical functions, such as numpy.mean() and numpy.var()
B) Use a traditional SQL database to compute statistics and then transfer results to the GPU
C) Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods
D) Use cuDF's built-in statistical functions like .mean(), .median(), and .var()

2. A manufacturing company wants to detect anomalies in a real-time IoT sensor dataset using NVIDIA technologies. The data arrives as a continuous stream, and the company needs to process it efficiently for immediate anomaly detection.
Which NVIDIA-powered approach is the most suitable for this use case?

A) Deploy an Autoencoder-based deep learning model using NVIDIA TensorRT for real-time inference
B) Use RAPIDS cuDF with batch processing to analyze historical anomalies every few hours
C) Apply cuML's Isolation Forest on incoming streaming data for real-time anomaly detection
D) Leverage cuGraph to model the sensor readings as a graph and detect anomalies using centrality measures

3. You are implementing a Dask-based solution for distributed data parallelism across a multi-GPU system.
Which configuration steps would ensure effective use of GPUs for parallel computation? (Select two)

A) Use dask_cuda's LocalCUDACluster with proper GPU memory management to handle multiple GPUs
B) Use dask_cuda's LocalCUDACluster and let Dask automatically allocate GPUs without any configuration
C) Create a LocalCUDACluster and manually specify the GPUs you want to use for each Dask worker
D) Use dask_cudf to convert DataFrame computations into GPU-accelerated operations using cuDF
E) Use Dask's Cluster class with the distributed scheduler and specify CPU cores only for GPU workloads

4. A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A) Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
B) Train a generative adversarial network (GAN) using PyTorch and then use the generated samples in RAPIDS AI without any additional processing.
C) Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
D) Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.

5. You are performing data cleansing on a large dataset using CuDF. The dataset contains numerical values, some of which are outliers. You need to remove or adjust these outliers to make your model training more robust.
Which of the following approaches should you consider for handling outliers efficiently in CuDF? (Select two)

A) Using applymap() to apply a custom function for handling outliers
B) Using clip() to set a maximum and minimum threshold for numerical values
C) Using quantile() to calculate the interquartile range (IQR) and filter out outliers
D) Using dropna() to remove rows with outliers

Solutions: