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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. Data caching and performance optimization 3. GPU-accelerated ETL design and implementation
Topic 2: 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 3: Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. cuDF and pandas data preprocessing 3. Synthetic data generation with RAPIDS
Topic 4: 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
Topic 5: Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Multi-GPU training comparison 3. Memory optimization techniques (mixed precision, batching) 4. Feature engineering
Topic 6: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Memory and capacity evaluation 3. Performance benchmarking and optimization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q215-Q220):

NEW QUESTION # 215
You are processing a dataset with billions of records and want to encode a categorical column efficiently using NVIDIA RAPIDS.
Which of the following methods correctly encodes categorical data using cuDF?

A. df['category_column'] = df['category_column'].apply(lambda x: hash(x) % 1000)
B. df['category_column'] = df['category_column'].astype('category')
C. df['category_column'] = df['category_column'].one_hot_encode()
D. df['category_column'] = LabelEncoder().fit_transform(df['category_column'])

Answer: B

NEW QUESTION # 216
A data scientist is working with large-scale datasets in a RAPIDS AI pipeline and needs to efficiently process and organize the data while leveraging GPU acceleration.
Which of the following approaches best ensures optimized processing and memory management when using NVIDIA technologies?

A. Store data in a pandas DataFrame first and then convert it to cuDF only when GPU operations are needed.
B. Write intermediate data to disk as CSV files before reading them back into RAPIDS AI to avoid memory overflow.
C. Process data using Apache Spark on CPU before transferring results to GPU for model training.
D. Use cuDF to store and process data in GPU memory, leveraging its vectorized operations for transformations and aggregations.

Answer: D

NEW QUESTION # 217
You are working with a large dataset containing 500 million records stored as a parquet file. Your task is to perform data filtering, aggregation, and transformation as efficiently as possible.
Which of the following approaches would be the best choice for accelerated data manipulation using NVIDIA technologies?

A. Convert the dataset to a NumPy array and use standard Python loops
B. Use RAPIDS cuDF to load, filter, and transform the dataset on the GPU
C. Use multiprocessing in Python to parallelize pandas operations across CPU cores
D. Load the dataset into pandas and use apply() for transformations

Answer: B

NEW QUESTION # 218
You are working on a data science project using NVIDIA RAPIDS on a multi-GPU system.
To ensure reproducibility and avoid software versioning conflicts, which of the following is the best approach for managing dependencies?

A. Install all required packages globally on the system using pip install without a virtual environment.
B. Use a Conda environment with RAPIDS-compatible versions of libraries installed using conda install
-c rapidsai -c nvidia.
C. Use a manually compiled CUDA installation alongside system-installed Python libraries to manage GPU dependencies.
D. Avoid dependency management frameworks and rely on manual tracking of package versions using a text file.

Answer: B

NEW QUESTION # 219
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. Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
B. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
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: A

NEW QUESTION # 220
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