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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
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. Perform time series analysis and visualization 3. Use cuGraph for graph analytics
MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Performance benchmarking and optimization 3. Model deployment in production environments
Machine Learning	15%	- Model Development and Optimization 1. Feature engineering 2. Hyperparameter tuning 3. Multi-GPU training comparison 4. Memory optimization techniques (mixed precision, batching)
Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. cuDF and pandas data preprocessing 3. Synthetic data generation with RAPIDS
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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q255-Q260):

NEW QUESTION # 255
A machine learning engineer is tasked with optimizing an image classification model on a cloud platform. The engineer must select a GPU-accelerated instance that balances cost and performance while ensuring compatibility with frameworks like TensorFlow and PyTorch.
Which instance configuration is the most appropriate choice?

A. A cloud instance with integrated graphics rather than dedicated NVIDIA GPUs.
B. A CPU-only instance with 128 GB of RAM for increased data processing speed.
C. A single-core CPU instance with high disk I/O throughput for faster data loading.
D. A cloud instance with NVIDIA A100 GPUs and NVLink support.

Answer: D

NEW QUESTION # 256
You are analyzing a large financial dataset containing stock market tick-by-tick data stored in a cuDF DataFrame. Since the dataset contains billions of data points, you need to aggregate it at the minute level before visualizing price trends efficiently.
Which of the following is the best approach for aggregating and visualizing this time-series data using NVIDIA technologies?

A. Use cuML's TSNE function to reduce dimensionality before visualizing with Bokeh
B. Load the data into a relational database (e.g., PostgreSQL), run an SQL query for aggregation, and visualize using Seaborn
C. Convert cuDF to Pandas, aggregate using .resample() in Pandas, and visualize using Matplotlib
D. Use cuDF's .groupby() function to aggregate at the minute level, then visualize using hvPlot

Answer: D

NEW QUESTION # 257
A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?

A. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
B. Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
C. Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.
D. Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.

Answer: B

NEW QUESTION # 258
You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A. Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing
B. Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
C. Use Dask's default scheduler to distribute work across GPUs without any specific configuration
D. Run the computation on a single GPU and let Dask handle CPU parallelism automatically
E. Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations

Answer: A,E

NEW QUESTION # 259
You are processing a large dataset with UNIX timestamps (seconds since Jan 1, 1970) ranging from Jan 1, 2000, to the present.
What is the most memory-efficient data type for the timestamp column in a GPU-accelerated cloud environment?

A. df['timestamp'] = df['timestamp'].astype('int64')
B. df['timestamp'] = df['timestamp'].astype('datetime64[ms]')
C. df['timestamp'] = df['timestamp'].astype('float32')
D. df['timestamp'] = df['timestamp'].astype('int32')

Answer: D

NEW QUESTION # 260
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