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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. Benchmarking GPU workflows 3. CRISP-DM workflow execution
Topic 2: 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
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: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Memory and capacity evaluation 3. Performance benchmarking and optimization
Topic 5: Machine Learning	15%	- Model Development and Optimization 1. Feature engineering 2. Multi-GPU training comparison 3. Hyperparameter tuning 4. Memory optimization techniques (mixed precision, batching)
Topic 6: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. Distributed data processing frameworks (Dask) 3. GPU-accelerated ETL design and implementation

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q186-Q191):

NEW QUESTION # 186
Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?

A. TensorRT automatically builds training models from raw data without requiring pre-trained models.
B. TensorRT optimizes deep learning models to run efficiently on NVIDIA GPUs by reducing precision while maintaining accuracy.
C. TensorRT is mainly used for data visualization and not for model inference.
D. TensorRT is only compatible with image classification models and does not support other model types.

Answer: B

NEW QUESTION # 187
A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.
Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?

A. Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.
B. Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.
C. Limit the benchmark to small datasets since GPUs excel at parallel processing.
D. Focus only on processing speed without considering resource consumption differences between frameworks.

Answer: A

NEW QUESTION # 188
A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A. Manually installing each package and its dependencies using pip
B. Using a single system-wide installation of CUDA and forcing all projects to use the same version
C. Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
D. Using only virtual environments (venv) without managing GPU dependencies separately

Answer: C

NEW QUESTION # 189
A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?

A. Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
B. Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.
C. Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.
D. Implement ETL processes using only SQL-based transformations within a relational database system.

Answer: B

NEW QUESTION # 190
You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?

A. df.to_float16()
B. df['col'].apply(lambda x: np.float16(x))
C. df.astype('float16')
D. df.convert_dtypes()

Answer: C

NEW QUESTION # 191
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