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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. You are managing a data processing pipeline that utilizes NVIDIA RAPIDS on GPUs for accelerated data transformations. During execution, you notice that the pipeline is not achieving expected performance gains.
What is the most effective approach to monitor and diagnose bottlenecks in this pipeline using NVIDIA technologies?

A) Reduce the dataset size and rerun the pipeline without profiling tools to check for performance improvements.
B) Enable RAPIDS memory pool logging to check for memory fragmentation and out-of-memory errors.
C) Use NVIDIA Nsight Systems to profile kernel execution times and memory transfers.
D) Run the pipeline on CPU instead of GPU to compare execution times.

3. You are preparing a dataset for training a machine learning model using NVIDIA RAPIDS cuML. The dataset contains a feature representing timestamps in nanoseconds.
To optimize GPU performance while ensuring precision, which data type should you choose?

A) bool - Provides a highly efficient way to store timestamps as binary values.
B) object - Allows flexibility in storing timestamps as strings for easier parsing.
C) datetime64[ns] - Optimizes storage and computation for timestamp data in RAPIDS.
D) int32 - Uses less memory and can store high-precision timestamps efficiently.

4. You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?

A) Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.
B) Compare training times only without considering throughput, power efficiency, or memory utilization.
C) Run each framework with default settings to compare their out-of-the-box performance without any optimizations.
D) Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.

5. You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?

A) Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
B) Split the dataset into multiple files based on a logical partitioning key, such as a date column.
C) Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.
D) Store the entire dataset in a single large file to minimize the number of files in the directory.

Solutions: