NVIDIA NCP-ADS

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?

A) Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.
B) Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.
C) Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
D) Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.

2. You are processing a large dataset using RAPIDS cuDF and Dask-cuDF on an NVIDIA GPU. Your profiling indicates that data transfer times between CPU and GPU are significantly slowing down your pipeline.
What is the most effective way to reduce this bottleneck?

A) Use cudf.read_parquet() instead of Pandas to load data directly into GPU memory
B) Transfer data in multiple smaller chunks to the GPU instead of larger batches
C) Increase the CPU RAM allocation to store more data before transferring to the GPU
D) Convert the dataset into a CSV format before transferring it to the GPU

3. You are working with a large dataset in a GPU-accelerated environment, and one of the columns, revenue, contains numeric values representing the annual revenue for companies. The revenue values are in the billions of dollars.
Which of the following is the most memory-efficient data type for the revenue column in a cuDF DataFrame?

A) df['revenue'] = df['revenue'].astype('uint32')
B) df['revenue'] = df['revenue'].astype('float32')
C) df['revenue'] = df['revenue'].astype('float64')
D) df['revenue'] = df['revenue'].astype('int64')

4. A data scientist is preprocessing a dataset containing several types of features:
A timestamp column storing millisecond-resolution timestamps.
A column with binary categorical values (Yes/No).
A column containing large continuous numerical values.
A column containing product category codes ranging from 0 to 5000.
Which of the following data type choices is the most optimal for maximizing GPU processing efficiency using NVIDIA cuDF?

A) Use string data type for timestamps, int32 for binary values, float16 for continuous numerical values, and int64 for product category codes.
B) Store timestamps as int64, encode binary values as float16, use float64 for continuous numerical values, and use int8 for product category codes.
C) Use float64 for timestamps, int8 for binary categorical values, float32 for continuous numerical values, and int32 for product category codes.
D) Convert timestamps to datetime64[ms], encode binary values as bool, use float32 for continuous values, and int16 for product category codes.
E) float32 is the best choice for large continuous numerical values, balancing precision and GPU efficiency.
F) Binary categorical values (Yes/No) should be stored as bool, which takes up minimal space.
G) Product category codes (range: 0-5000) fit within int16 (which can hold values from -32,768 to
32,767), making it more memory-efficient than int32.

5. A company is processing large log files from a cloud application, accumulating over 5TB of data daily. The data processing pipeline must be GPU-accelerated to extract insights quickly.
Which of the following is the most effective approach to handle high-volume log processing using NVIDIA technologies?

A) Use cuDF with explicit memory management to load and process the entire dataset into a single GPU.
B) Store logs as Pandas DataFrames and use multiprocessing to parallelize operations across CPU cores.
C) Use RAPIDS cuML for performing log file processing, taking advantage of its optimized ML algorithms.
D) Leverage Dask-cuDF to distribute the dataset across multiple GPUs, ensuring efficient parallel processing.

Solutions: