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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q174-Q179):

NEW QUESTION # 174
Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?

A. Automatically adjust the learning rate based on the model's convergence
B. Modify the training dataset during model execution
C. Increase batch size to improve accuracy
D. Visualize GPU memory utilization over time

Answer: D

NEW QUESTION # 175
You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A. Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
B. Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
C. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
D. Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.

Answer: A

NEW QUESTION # 176
You are consulting for a retail company that collects data from daily sales transactions, customer interactions, and inventory tracking across multiple locations. They are unsure whether their dataset qualifies as big data and which processing method would be most suitable.
Which of the following characteristics best indicate that the dataset requires big data processing and acceleration techniques?

A. The dataset contains more than 1 million records, making it impossible to process using pandas
B. The dataset exceeds the memory capacity of a single machine and requires distributed or GPU- accelerated processing
C. The dataset is stored in multiple relational database tables, making querying inefficient
D. The dataset includes a mix of numerical and categorical variables, requiring additional preprocessing

Answer: B

NEW QUESTION # 177
You are working with large datasets in cuDF and have noticed significant performance bottlenecks due to repeated computation and excessive shuffling in your workflow. You want to use data caching to optimize the execution plan and reduce redundant operations.
Which of the following is the best way to implement data caching in cuDF to avoid repeated recomputation and excessive shuffling?

A. Store intermediate DataFrames as temporary CSV files on disk and reload them when needed to simulate a cache.
B. Use the .persist() method on a cuDF DataFrame to store intermediate results in GPU memory, reducing redundant calculations.
C. Use the .reset_index() method after performing transformations to force materialization of the dataset and cache results.
D. Use the .to_pandas() method to convert the cuDF DataFrame into a Pandas DataFrame, leveraging CPU caching instead of GPU caching.

Answer: B

NEW QUESTION # 178
You are working on a large-scale graph analysis problem that involves computing the shortest paths between nodes in a massive social network dataset. You decide to leverage NVIDIA RAPIDS cuGraph for accelerated computation.
Which of the following cuGraph functions should you use?

A. cugraph.k_truss()
B. cugraph.label_propagation()
C. cugraph.pagerank()
D. cugraph.sssp()

Answer: D

NEW QUESTION # 179
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