NVIDIA NCP-ADS Exam | NCP-ADS PDF Guide - Ensure You Pass NCP-ADS Exam For Sure

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: MLOps	19%	- Monitoring, logging and maintenance - End-to-end workflow management - Pipeline automation and orchestration - Model deployment and serving
Topic 2: GPU and Cloud Computing	16%	- Resource management and scaling strategies - CRISP-DM and data science methodology - Cloud GPU environments and deployment - GPU architecture and acceleration principles
Topic 3: Data Manipulation and Software Literacy	19%	- Performance profiling and optimization tools - GPU-accelerated ETL workflows - Data processing libraries selection and usage - Dependency management and containerization
Topic 4: Data Preparation	17%	- Feature engineering and data type optimization - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Data validation and quality assurance
Topic 5: Machine Learning	15%	- Model training and hyperparameter tuning - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms - Distributed training strategies
Topic 6: Data Analysis	14%	- Distributed and parallel data processing - Data visualization and graph analytics - Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q175-Q180):

NEW QUESTION # 175
A machine learning engineer is working on an image classification problem where the dataset is small and lacks variability. To improve generalization, the engineer decides to augment the dataset using NVIDIA RAPIDS.
What is the best method to generate synthetic data efficiently while leveraging GPU acceleration?

A. Use cuML.PCA() to reduce dimensionality and create synthetic samples by reconstructing the data with added noise.
B. Apply cuML.GaussianMixture() to generate new synthetic data points based on an estimated probability distribution.
C. Use traditional CPU-based augmentation techniques like OpenCV to transform images and generate new data.
D. Use cuDF with cudf.DataFrame.sample() to create new samples by randomly selecting existing rows.

Answer: B

NEW QUESTION # 176
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 a single system-wide installation of CUDA and forcing all projects to use the same version
B. Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
C. Manually installing each package and its dependencies using pip
D. Using only virtual environments (venv) without managing GPU dependencies separately

Answer: B

NEW QUESTION # 177
You are training a machine learning model using NVIDIA RAPIDS cuML and notice that the training process is significantly slower than expected. You suspect that there are bottlenecks in data movement and computation.
Which of the following techniques can best help you diagnose and resolve these bottlenecks?

A. Use cudf.DataFrame.to_pandas() to convert the dataset to a pandas DataFrame for analysis.
B. Use cuml.common.device_auto_mem_size() to check GPU memory usage and adjust batch sizes accordingly.
C. Reduce the number of features used in training without profiling the actual bottlenecks.
D. Move all data from GPU memory to CPU memory before training the model.

Answer: B

NEW QUESTION # 178
A research team is analyzing large-scale social interactions and wants to identify strongly connected communities within a massive graph dataset using NVIDIA's cuGraph library.
Which method would be the most efficient for this task?

A. Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.
B. Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.
C. Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.
D. Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.

Answer: B

NEW QUESTION # 179
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. Use multiprocessing in Python to parallelize pandas operations across CPU cores
B. Convert the dataset to a NumPy array and use standard Python loops
C. Use RAPIDS cuDF to load, filter, and transform the dataset on the GPU
D. Load the dataset into pandas and use apply() for transformations

Answer: C

NEW QUESTION # 180
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