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How to prepare for the NCP-ADS actual test? The following may give you some guidance.

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with structured tabular data in a cloud-based GPU environment.
Your dataset contains the following columns:
Column Name Example Values Data Type Needed
user_id 15432, 98765, 43210 Integer
purchase_amt 12.99, 35.50, 100.75 Floating Point
category 'Books', 'Electronics' Categorical
Which of the following is the most optimal approach to assign data types to these columns to ensure efficient memory usage and computational performance?

A) 1. df['user_id'] = df['user_id'].astype('int32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float32')
3. df['category'] = df['category'].astype('category')
B) 1. df['user_id'] = df['user_id'].astype('float32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
C) 1. df['user_id'] = df['user_id'].astype('int64')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
D) 1. df['user_id'] = df['user_id'].astype('int16')
2. df['purchase_amt'] = df['purchase_amt'].astype('float16')
3. df['category'] = df['category'].astype('string')

2. A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A) Manually installing each package and its dependencies using pip
B) Using a single system-wide installation of CUDA and forcing all projects to use the same version
C) Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
D) Using only virtual environments (venv) without managing GPU dependencies separately

3. A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A) Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.
B) Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.
C) Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
D) Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
E) Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
F) Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.

4. You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

A) Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.
B) Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.
C) Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.
D) Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.

5. Which of the following best describes the functionality of DLProf in deep learning model profiling?

A) DLProf can only profile the CPU side of a deep learning model's performance.
B) DLProf is used for visualizing model predictions, similar to TensorBoard.
C) DLProf uses statistical methods to predict future model performance based on historical data.
D) DLProf tracks GPU utilization, memory bandwidth, and kernel execution time to identify performance bottlenecks.

Solutions: