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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation | 17% | - Data Cleaning and Transformation
|
| Topic 2: GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Topic 3: Machine Learning | 15% | - Model Development and Optimization
|
| Topic 4: MLOps | 19% | - Deployment and Monitoring
|
| Topic 5: Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Topic 6: Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. A data scientist is working with a large dataset that contains string-based numeric values that need to be converted to floating-point numbers for further analysis. The dataset is stored as a cuDF DataFrame, and the scientist needs to ensure the conversion is performed optimally on a GPU.
Which of the following is the best method for converting string-based numeric values to floating-point numbers using NVIDIA-accelerated processing?
A) Use NumPy's astype(float) method after converting the cuDF DataFrame into a NumPy array.
B) Use pandas.to_numeric() since pandas automatically handles type conversion.
C) Use cudf.DataFrame.astype(float) to convert string values to floating-point numbers efficiently on a GPU.
D) Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.
2. You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?
A) df = pd.read_parquet("data.parquet")
B) df = cudf.read_parquet("data.parquet")
C) df = cudf.read_csv("data.parquet")
D) df = cudf.to_gpu(pd.read_parquet("data.parquet"))
3. A team is processing large-scale tabular data using cuDF and cuML on NVIDIA GPUs but is facing performance degradation.
Which of the following techniques would be the most effective in identifying and resolving bottlenecks in the pipeline?
A) Convert all datasets into pandas DataFrames for initial processing before moving to cuDF.
B) Increase GPU clock speed manually to force higher processing power.
C) Use nvprof or nsight compute to analyze kernel execution time and memory transfer efficiency.
D) Reduce the dataset size to a smaller sample to speed up processing.
4. When scaling a distributed data processing framework using NVIDIA GPU technology for big data processing, which of the following factors is most critical to optimize performance?
A) Using more CPU cores to handle computation-heavy tasks.
B) Maximizing the amount of data transferred between GPUs for faster processing.
C) Limiting the number of GPU nodes used in the cluster to avoid complexity.
D) Ensuring the proper configuration of GPU resources across all nodes in the distributed system.
5. You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?
A) Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
B) Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.
C) Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
D) Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: D |
