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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.
Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?

A) Limit the benchmark to small datasets since GPUs excel at parallel processing.
B) Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.
C) Focus only on processing speed without considering resource consumption differences between frameworks.
D) Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.

2. You need to set up an isolated, GPU-accelerated environment for a deep learning project that requires specific CUDA, cuDNN, and RAPIDS versions.
Which of the following best ensures a reproducible environment using Docker?

A) Install NVIDIA drivers manually inside a Docker container every time it runs.
B) Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.
C) Use a system-wide CUDA installation and mount the /usr/local/cuda directory into the container to provide GPU support.
D) Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.

3. You are training a deep learning model on a large dataset of images stored in an Amazon S3 bucket.
You want to optimize data loading, augmentation, and preprocessing on NVIDIA GPUs to avoid CPU bottlenecks.
Which of the following approaches is the most efficient for GPU-accelerated data preprocessing?

A) Use NVIDIA DALI to decode images, apply transformations such as resizing and normalization, and load batches directly to the GPU for training.
B) Load the dataset using PyTorch's torchvision.transforms and DataLoader, leveraging the CPU for data preprocessing and transferring batches to the GPU before training.
C) Use TensorFlow's tf.data API with tf.image transformations and ensure that the preprocessed images are transferred to GPU memory at the end of the pipeline.
D) Use OpenCV to load and preprocess images on the CPU, then transfer the processed images to the GPU before training.

4. You are tasked with processing a large dataset of 100 million records for a deep learning project using NVIDIA technologies. You need to determine the most efficient data processing library for this task to maximize performance and reduce processing time.
Which of the following libraries is best suited for this task?

A) Dask
B) cuDF
C) PySpark
D) pandas

5. A data scientist is analyzing sales data for an e-commerce company that experiences strong seasonal trends (e.g., increased sales during holiday seasons). The goal is to accurately forecast future sales using GPU-accelerated data science techniques.
Which approach would be the most effective?

A) Apply k-Means clustering to identify seasonal patterns and extrapolate future values.
B) Use a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and optimize it using NVIDIA RAPIDS.
C) Compute a rolling average and manually adjust for seasonality based on previous peak sales months.
D) Train a Decision Tree model using cuML to classify future sales trends.

Solutions: