Vce NCP-ADS Exam | NCP-ADS Exam Study Solutions

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. Data caching and performance optimization 3. GPU-accelerated ETL design and implementation
Topic 2: MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Model deployment in production environments 3. Performance benchmarking and optimization
Topic 3: Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Hyperparameter tuning 3. Multi-GPU training comparison 4. Feature engineering
Topic 4: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Use cuGraph for graph analytics 2. Detect anomalies in time series datasets 3. Perform time series analysis and visualization
Topic 5: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. CRISP-DM workflow execution 3. Benchmarking GPU workflows
Topic 6: Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Synthetic data generation with RAPIDS 3. Data normalization and standardization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q147-Q152):

NEW QUESTION # 147
A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

A. Perform all analysis on the CPU to avoid potential GPU memory limitations.
B. Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.
C. Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
D. Downsample the dataset and analyze a subset using Pandas for efficiency.

Answer: C

NEW QUESTION # 148
You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A. Apply a deep learning-based imputation model before moving data to the GPU
B. Use fillna() with a fixed value on the GPU using cuDF
C. Drop all rows containing missing values using Pandas before transferring data to the GPU
D. Convert the dataset to a NumPy array and manually replace missing values with the mean

Answer: B

NEW QUESTION # 149
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. Using only virtual environments (venv) without managing GPU dependencies separately
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. Manually installing each package and its dependencies using pip

Answer: C

NEW QUESTION # 150
You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.
What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

A. Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
B. Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
C. Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
D. Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
E. Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.

Answer: A,C

NEW QUESTION # 151
You are working with a large dataset containing 500 million records stored as a parquet file. Your task is to perform data filtering, aggregation, and transformation as efficiently as possible.
Which of the following approaches would be the best choice for accelerated data manipulation using NVIDIA technologies?

A. Load the dataset into pandas and use apply() for transformations
B. Convert the dataset to a NumPy array and use standard Python loops
C. Use multiprocessing in Python to parallelize pandas operations across CPU cores
D. Use RAPIDS cuDF to load, filter, and transform the dataset on the GPU

Answer: D

NEW QUESTION # 152
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