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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation | 17% | - Data cleaning and quality handling
|
| Topic 2: MLOps | 19% | - Model monitoring and management
|
| Topic 3: GPU and Cloud Computing | 16% | - Cloud GPU environments
|
| Topic 4: Machine Learning | 15% | - Model training with GPU acceleration
|
| Topic 5: Data Analysis | 14% | - Time-series analysis
|
| Topic 6: Data Manipulation and Software Literacy | 19% | - Software literacy and development tools
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are consulting for a retail company that collects data from daily sales transactions, customer interactions, and inventory tracking across multiple locations. They are unsure whether their dataset qualifies as big data and which processing method would be most suitable.
Which of the following characteristics best indicate that the dataset requires big data processing and acceleration techniques?
- A. The dataset contains more than 1 million records, making it impossible to process using pandas
- B. The dataset includes a mix of numerical and categorical variables, requiring additional preprocessing
- C. The dataset exceeds the memory capacity of a single machine and requires distributed or GPU- accelerated processing
- D. The dataset is stored in multiple relational database tables, making querying inefficient
Correct Answer: C 🗳️
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 = cudf.read_csv("data.parquet")
- B. df = pd.read_parquet("data.parquet")
- C. df = cudf.to_gpu(pd.read_parquet("data.parquet"))
- D. df = cudf.read_parquet("data.parquet")
Correct Answer: D 🗳️
A machine learning engineer is working with a 1 TB dataset stored in Apache Parquet format and wants to analyze the data for patterns before building a model. The engineer is considering various acceleration methods.
Which of the following approaches would be the best choice for efficient analysis?
- A. Convert the Parquet file to a Pandas DataFrame and perform analysis using Pandas functions.
- B. Read the Parquet file line by line using Python's built-in file handling functions to save memory.
- C. Load the dataset into a relational database and query it using simple SQL statements.
- D. Use a GPU-accelerated library such as RAPIDS cuDF to load and process the Parquet file efficiently.
Correct Answer: D 🗳️
You are tasked with profiling a deep learning model using NVIDIA's DLProf to identify performance bottlenecks and optimize resource utilization.
Which of the following statements correctly describes the capabilities of DLProf?
- A. DLProf is primarily designed for debugging model accuracy rather than performance analysis.
- B. DLProf only works with TensorFlow models and does not support PyTorch-based workloads.
- C. DLProf requires significant modifications to the source code to collect profiling data.
- D. DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.
Correct Answer: D 🗳️
You are working with a deep learning model for real-time inference on an NVIDIA GPU. Your objective is to optimize inference speed while maintaining acceptable model accuracy.
Which of the following techniques provides the best balance between inference performance and accuracy?
- A. Increase the number of neurons in each layer to improve the model's accuracy
- B. Use TensorRT for model optimization, including precision quantization and layer fusion
- C. Use double-precision (FP64) computations to prevent numerical instability in inference
- D. Disable batch inference to ensure each sample is processed independently for better accuracy
Correct Answer: B 🗳️
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