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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Memory and capacity evaluation 3. Performance benchmarking and optimization
Topic 2: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Perform time series analysis and visualization 3. Use cuGraph for graph analytics
Topic 3: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. GPU-accelerated ETL design and implementation 3. Distributed data processing frameworks (Dask)
Topic 4: 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 5: Machine Learning	15%	- Model Development and Optimization 1. Feature engineering 2. Memory optimization techniques (mixed precision, batching) 3. Hyperparameter tuning 4. Multi-GPU training comparison
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 (Q275-Q280):

NEW QUESTION # 275
You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?

A. Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.
B. Convert categorical variables into int8 to optimize GPU memory usage.
C. Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.
D. Convert all numerical data to float64 for maximum precision in calculations.

Answer: C

NEW QUESTION # 276
A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.
Which of the following approaches would best help identify the source of the bottleneck?

A. Rely on nvidia-smi alone to check GPU utilization without considering other profiling tools.
B. Use nsys (NVIDIA Nsight Systems) to profile GPU kernel execution and memory transfer times.
C. Disable GPU parallelism and run computations on the CPU to compare performance.
D. Increase the batch size of data being processed without analyzing memory utilization.

Answer: B

NEW QUESTION # 277
You are setting up a GPU-accelerated data science environment on a cloud-based instance that utilizes NVIDIA GPUs.
To ensure compatibility between CUDA, RAPIDS, and Python libraries, which of the following is the most effective approach for managing dependencies and avoiding version conflicts?

A. Install RAPIDS, PyTorch, TensorFlow, and other libraries in the same Conda base environment for simplified access
B. Use Conda environments with version-pinned dependencies to create an isolated environment for RAPIDS and CUDA
C. Manually install required libraries in a system-wide Python environment using pip install without version specifications
D. Use a virtual machine with all dependencies pre-installed to avoid any software conflicts

Answer: B

NEW QUESTION # 278
You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A. df['temperature'].dropna(inplace=True)
B. 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)
C. df['temperature'].fillna(df['temperature'].median(), inplace=True)
D. df['temperature'].fillna(df['temperature'].mean(), inplace=True)

Answer: C

NEW QUESTION # 279
You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?

A. cuGraph enables parallel computation on the GPU, significantly speeding up community detection tasks compared to traditional CPU-based implementations.
B. Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.
C. cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.
D. cuGraph primarily accelerates deep learning tasks and is not optimized for graph-based algorithms.

Answer: A

NEW QUESTION # 280
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