NVIDIA NCP-ADS

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NVIDIA NCP-ADS Exam Syllabus Topics:

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('int64')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
B) 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')
C) 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')
D) 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')

2. Which of the following statements best describes the role of GPUs in accelerating data science workloads?

A) GPUs are designed primarily for rendering graphics and have limited utility in machine learning and deep learning applications.
B) GPUs use thousands of smaller cores that can execute many parallel computations simultaneously, making them ideal for large-scale matrix operations.
C) GPUs are optimized for sequential data processing tasks, making them more efficient than CPUs for database operations.
D) GPUs are only effective for acceleration when used in conjunction with Tensor Processing Units (TPUs), as they cannot train deep learning models independently.

3. You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?

A) Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.
B) Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.
C) Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
D) Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.

4. You are working on a financial fraud detection system using NVIDIA RAPIDS cuML. You have a large time-series dataset of transaction amounts over time, and you need to identify anomalies such as unusual spikes in transactions.
Which of the following approaches is the most appropriate for detecting anomalies using NVIDIA technologies?

A) Use cuML's DBSCAN clustering to identify anomalies based on density differences
B) Run standard Pandas-based anomaly detection methods on CPU for better precision
C) Train a deep learning model with TensorFlow and deploy it using NVIDIA Triton Inference Server for anomaly detection
D) Apply cuML's PCA-based anomaly detection to detect outliers in the principal component space

5. You are developing an AI model for medical imaging that requires acquiring a large dataset of MRI scans from multiple sources.
Which NVIDIA technology would best assist in acquiring, standardizing, and efficiently handling the dataset?

A) Use NVIDIA Morpheus to analyze MRI data for cybersecurity threats before storing it.
B) Use NVIDIA Nsight Systems to collect MRI data and visualize potential performance bottlenecks.
C) Use NVIDIA Clara Imaging to standardize, curate, and preprocess MRI datasets for AI model training.
D) Use NVIDIA AI Enterprise to directly acquire and store MRI images from multiple hospitals.

Solutions: