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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. 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) Read the Parquet file line by line using Python's built-in file handling functions to save memory.
B) Use a GPU-accelerated library such as RAPIDS cuDF to load and process the Parquet file efficiently.
C) Load the dataset into a relational database and query it using simple SQL statements.
D) Convert the Parquet file to a Pandas DataFrame and perform analysis using Pandas functions.

3. You are working with a dataset containing 2 billion rows of financial transactions, and you need to perform exploratory data analysis (EDA) before building a predictive model.
Which of the following approaches is the most appropriate for handling this data efficiently?

A) Use SQLite to store the data locally and run queries sequentially to minimize memory consumption.
B) Convert the dataset to JSON format and use Python's built-in json module to parse and analyze it efficiently.
C) Load the entire dataset into a Pandas DataFrame and analyze it using Pandas built-in methods.
D) Use an accelerated data science framework such as RAPIDS cuDF or Dask to distribute computations across GPUs.

4. 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) Dask
B) Vaex
C) Pandas
D) PySpark

5. You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A) Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
B) Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing
C) Use Dask's default scheduler to distribute work across GPUs without any specific configuration
D) Run the computation on a single GPU and let Dask handle CPU parallelism automatically
E) Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations

Solutions: