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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. Data normalization and standardization 3. cuDF and pandas data preprocessing
Topic 2: MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Performance benchmarking and optimization 3. Model deployment in production environments
Topic 3: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. CRISP-DM workflow execution 2. Docker and Conda environment management 3. Benchmarking GPU workflows
Topic 4: 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
Topic 5: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Use cuGraph for graph analytics 3. Perform time series analysis and visualization
Topic 6: Machine Learning	15%	- Model Development and Optimization 1. Multi-GPU training comparison 2. Memory optimization techniques (mixed precision, batching) 3. Feature engineering 4. Hyperparameter tuning

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q120-Q125):

NEW QUESTION # 120
You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?

A. Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs
B. Using Apache Spark with PySpark for CPU-based distributed data processing
C. Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing
D. Using TensorFlow's Dataset API to load and preprocess the data on GPUs

Answer: C

NEW QUESTION # 121
You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

A. Perform the join operation entirely in a CPU-based Spark environment for better stability
B. Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF
C. Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
D. Convert both DataFrames to Pandas before performing the join for compatibility

Answer: C

NEW QUESTION # 122
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A. Better for memory-intensive workloads
B. Efficient handling of matrix operations in machine learning models
C. Faster parallel processing for large datasets
D. Lower cost of cloud infrastructure
E. Lower energy consumption compared to CPUs

Answer: B,C

NEW QUESTION # 123
After profiling a deep learning model using NVIDIA DLProf, you notice that a specific GEMM (General Matrix Multiplication) operation takes significantly longer than expected. The profiler output reveals that tensor cores are underutilized despite having an Ampere-based GPU with Tensor Cores enabled.
Which of the following actions is the MOST appropriate to improve performance?

A. Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.
B. Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.
C. Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).
D. Switch from stochastic gradient descent (SGD) to Adam optimizer, as Adam improves convergence and computational efficiency.

Answer: C

NEW QUESTION # 124
A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

A. Use cuDF's built-in statistical functions like .mean(), .median(), and .var()
B. Use NumPy's statistical functions, such as numpy.mean() and numpy.var()
C. Use a traditional SQL database to compute statistics and then transfer results to the GPU
D. Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods

Answer: A

NEW QUESTION # 125
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