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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: 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 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: Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Memory optimization techniques (mixed precision, batching) 3. Multi-GPU training comparison 4. Feature engineering
Topic 5: MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Performance benchmarking and optimization 3. Model deployment in production environments
Topic 6: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Use cuGraph for graph analytics 2. Detect anomalies in time series datasets 3. Perform time series analysis and visualization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q39-Q44):

NEW QUESTION # 39
You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?

A. Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.
B. Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.
C. Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.
D. Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.

Answer: D

NEW QUESTION # 40
A data scientist is analyzing sales data for an e-commerce company that experiences strong seasonal trends (e.g., increased sales during holiday seasons). The goal is to accurately forecast future sales using GPU-accelerated data science techniques.
Which approach would be the most effective?

A. Apply k-Means clustering to identify seasonal patterns and extrapolate future values.
B. Use a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and optimize it using NVIDIA RAPIDS.
C. Compute a rolling average and manually adjust for seasonality based on previous peak sales months.
D. Train a Decision Tree model using cuML to classify future sales trends.

Answer: B

NEW QUESTION # 41
Which of the following hardware components is most critical for accelerating the training of deep learning models?

A. Graphics Processing Unit (GPU)
B. Central Processing Unit (CPU)
C. Random Access Memory (RAM)
D. Solid State Drive (SSD)

Answer: A

NEW QUESTION # 42
You are working with a large dataset containing customer transactions and want to perform exploratory data analysis (EDA) efficiently. Given the dataset's size, you decide to use NVIDIA RAPIDS to accelerate the process.
Which of the following approaches is the most effective for conducting EDA using NVIDIA technologies?

A. Load the dataset into Pandas and use Matplotlib for visualization
B. Use TensorFlow and Keras to preprocess the data before performing EDA
C. Use RAPIDS cuDF to perform fast dataframe operations and visualize results with cuXfilter
D. Perform SQL queries on a CPU-based database for initial data analysis before GPU acceleration

Answer: C

NEW QUESTION # 43
You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric features are standardized for better model performance.
Which of the following is the best approach for scaling data using RAPIDS?

A. df_scaled = scaler.fit_transform(df)
B. scaler = cuml.preprocessing.StandardScaler()
C. df_scaled = (df - df.min()) / (df.max() - df.min())
D. df_scaled = df / df.max()
E. df_scaled = df.apply(lambda x: x / np.linalg.norm(x))

Answer: B

NEW QUESTION # 44
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