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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Analysis | 14% | - Data visualization and graph analytics - Distributed and parallel data processing - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) |
| Data Manipulation and Software Literacy | 19% | - Data processing libraries selection and usage - Dependency management and containerization - GPU-accelerated ETL workflows - Performance profiling and optimization tools |
| Data Preparation | 17% | - Workflow monitoring and bottleneck identification - Data validation and quality assurance - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization |
| MLOps | 19% | - Model deployment and serving - Monitoring, logging and maintenance - Pipeline automation and orchestration - End-to-end workflow management |
| GPU and Cloud Computing | 16% | - GPU architecture and acceleration principles - Resource management and scaling strategies - CRISP-DM and data science methodology - Cloud GPU environments and deployment |
| Machine Learning | 15% | - Model training and hyperparameter tuning - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms - Distributed training strategies |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. A retail company needs to design a real-time ETL workflow to process millions of sales records per second from multiple sources. The data needs to be cleaned, aggregated, and stored for real-time analytics.
Which NVIDIA-powered solution is best suited for this workload?
A) Use NumPy to perform array-based operations on sales data before inserting into a database
B) Use standard Apache Spark on CPU clusters and move transformed data to GPU storage
C) Use NVIDIA Spark RAPIDS to accelerate ETL processing within Apache Spark on GPUs
D) Perform ETL with cuGraph to model sales data as a graph before storage
2. A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?
A) Prefetching data with the CPU while training on the GPU
B) Using mixed-precision training with Tensor Cores
C) Disabling data parallelism
D) Running training exclusively on CPU
3. You need to process a dataset containing 10 billion rows, applying complex transformations and aggregations. The dataset does not fit into RAM, and you need an efficient, scalable solution for parallel processing.
Which of the following is the best choice?
A) Pandas
B) Vaex
C) PySpark
D) Dask
4. You are selecting a dataset for GPU-accelerated data science using NVIDIA RAPIDS.
Which dataset characteristic is most suitable for taking advantage of GPU acceleration?
A) A dataset consisting of unstructured text stored as plain .txt files
B) A dataset stored as an SQLite database file
C) A large, structured dataset stored in a Parquet format
D) A small dataset with fewer than 1,000 rows stored in an Excel file
5. You are working on a data science project that requires processing a large-scale dataset stored in CSV format. The dataset contains hundreds of millions of rows, and you want to load it efficiently into NVIDIA RAPIDS cuDF for accelerated processing on a GPU.
Which of the following approaches is the most optimal way to load the dataset?
A) import cudf 2. df = cudf.DataFrame.from_pandas(pd.read_csv("large_dataset.csv"))
B) import cudf 2. df = cudf.read_csv("large_dataset.csv", chunksize=100000)
C) import pandas as pd 2. df = pd.read_csv("large_dataset.csv")
D) import dask_cudf 2. df = dask_cudf.read_csv("large_dataset.csv")
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: D |
