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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q160-Q165):

NEW QUESTION # 160
You are working with a social network dataset containing millions of user interactions and need to identify influential users based on their connectivity and interactions.
Which approach using NVIDIA's cuGraph library is the most appropriate for this task?

A. Use cuGraph's DBSCAN clustering to detect communities in the social network.
B. Use cuGraph's PageRank algorithm to rank users based on their importance in the network.
C. Apply cuGraph's K-Means clustering to group users with similar connectivity patterns.
D. Run a breadth-first search (BFS) on the entire graph to find the most influential users.

Answer: B

NEW QUESTION # 161
You are analyzing a dataset that contains missing values.
Which of the following techniques is most appropriate when dealing with missing numerical data in a dataset, ensuring minimal impact on model performance?

A. Replacing missing values with the mean of the column
B. Replacing missing values with a constant value (e.g., zero)
C. Removing rows with missing values
D. Using k-nearest neighbors (KNN) imputation

Answer: D

NEW QUESTION # 162
You are training a convolutional neural network (CNN) model with a large dataset on a single GPU.
The model is consuming too much GPU memory, and training is slow.
Which of the following techniques would help you reduce GPU memory consumption while maintaining or improving the efficiency of training? (Select two)

A. Use automatic differentiation for every operation to save memory during the backpropagation phase.
B. Use batch normalization to reduce memory usage by skipping some of the computations during training.
C. Decrease the size of the model by reducing the number of layers or the number of filters per layer.
D. Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.
E. Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.

Answer: D,E

NEW QUESTION # 163
A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance.
To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?

A. Store all training data in RAM and load it directly to the CPU for processing.
B. Increase the number of CPU cores and distribute training across multiple CPU threads.
C. Disable CUDA and use only OpenMP to parallelize computations across CPU cores.
D. Use a cloud instance with multiple GPUs and enable mixed-precision training.

Answer: D

NEW QUESTION # 164
You are tasked with profiling a deep learning model using NVIDIA's DLProf to identify performance bottlenecks and optimize resource utilization.
Which of the following statements correctly describes the capabilities of DLProf?

A. DLProf requires significant modifications to the source code to collect profiling data.
B. DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.
C. DLProf only works with TensorFlow models and does not support PyTorch-based workloads.
D. DLProf is primarily designed for debugging model accuracy rather than performance analysis.

Answer: B

NEW QUESTION # 165
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