NCP-ADS Valid Exam Duration & Test NCP-ADS Practice

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. cuDF and pandas data preprocessing 3. Data normalization and standardization
Machine Learning	15%	- Model Development and Optimization 1. Feature engineering 2. Multi-GPU training comparison 3. Hyperparameter tuning 4. Memory optimization techniques (mixed precision, batching)
MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Model deployment in production environments 3. Memory and capacity evaluation
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. GPU-accelerated ETL design and implementation 2. Distributed data processing frameworks (Dask) 3. Data caching and performance optimization
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Benchmarking GPU workflows 2. Docker and Conda environment management 3. CRISP-DM workflow execution
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Use cuGraph for graph analytics 3. Perform time series analysis and visualization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q156-Q161):

NEW QUESTION # 156
A financial services company is deploying an AI-driven risk assessment model using NVIDIA GPUs on a cloud platform. To optimize resource utilization and cost efficiency, they need to determine the best GPU deployment strategy.
Which of the following is the most effective approach?

A. Run all AI workloads on a single large GPU instance without any partitioning or workload separation.
B. Deploy separate full-GPU instances for each workload, even if they have variable compute demands.
C. Choose an on-demand cloud instance with an outdated GPU model to reduce costs, even if performance is compromised.
D. Deploy the model using NVIDIAAI Enterprise with MIG (Multi-Instance GPU) to allocate multiple workloads on a single GPU efficiently.

Answer: D

NEW QUESTION # 157
You are working with a large dataset containing customer transactions and want to perform exploratory data analysis (EDA) efficiently. Given the dataset's size, you decide to use NVIDIA RAPIDS to accelerate the process.
Which of the following approaches is the most effective for conducting EDA using NVIDIA technologies?

A. Load the dataset into Pandas and use Matplotlib for visualization
B. Perform SQL queries on a CPU-based database for initial data analysis before GPU acceleration
C. Use TensorFlow and Keras to preprocess the data before performing EDA
D. Use RAPIDS cuDF to perform fast dataframe operations and visualize results with cuXfilter

Answer: D

NEW QUESTION # 158
A machine learning engineer is working on a dataset with thousands of numerical features. The dataset is too large for standard CPU-based processing, so the engineer decides to leverage GPUs for efficient feature engineering.
Which of the following techniques is the most suitable for dimensionality reduction using GPU acceleration?

A. Applying RAPIDS cuML's PCA (Principal Component Analysis) to reduce feature dimensions efficiently.
B. Encoding all numerical features as categorical variables to reduce dimensionality.
C. Converting the dataset into a sparse matrix and running traditional singular value decomposition (SVD) on a CPU.
D. Using manual feature selection by dropping columns with low variance on a CPU.

Answer: A

NEW QUESTION # 159
You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?

A. Use cuDF to manipulate data distributions and generate new data points based on existing features.
B. Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.
C. Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.

Answer: A

NEW QUESTION # 160
When using cuDF from NVIDIA RAPIDS for GPU-accelerated data manipulation, which of the following operations will not efficiently utilize the GPU?

A. Performing a group-by operation followed by an aggregation.
B. Filtering a large dataset based on a condition (e.g., column > 1000).
C. Merging two large datasets on a common column.
D. Applying a Python lambda function row-wise to a DataFrame.

Answer: D

NEW QUESTION # 161
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