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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. When comparing the required memory with the available memory on a GPU for an MLOps deployment using NVIDIA technologies, which of the following is the best method to optimize memory usage while training large models?

A) Decrease the number of training iterations to reduce memory consumption.
B) Use mixed-precision training to reduce memory requirements by using half-precision floating-point numbers.
C) Increase the input data size to fully utilize available memory and improve training performance.
D) Use a higher number of GPUs to distribute the model and memory load across the GPUs.

2. You are working with a dataset consisting of 100 million records stored in a distributed system. The dataset includes numerical and categorical variables, requiring both exploratory data analysis (EDA) and machine learning model training. The processing time using traditional CPU-based methods is too slow.
Which of the following techniques would be the most effective acceleration method to handle this workload efficiently?

A) Scale up to a high-core-count CPU machine
B) Use RAPIDS cuDF for GPU-accelerated data processing
C) Reduce the dataset to a smaller sample size before processin
D) Store the dataset in a relational database and query it using SQL

3. A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.
Which of the following approaches is the most efficient for visualizing this time-series data?

A) Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.
B) Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.
C) Use seaborn with a sampled subset of the dataset to generate line plots.
D) Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.

4. You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A) Perform all EDA using NumPy and SciPy for optimized array computations.
B) Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
C) Use Pandas directly for data manipulation and visualization.
D) Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.

5. 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 cudf.DataFrame.astype(float) to convert string values to floating-point numbers efficiently on a GPU.
B) Use pandas.to_numeric() since pandas automatically handles type conversion.
C) Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.
D) Use NumPy's astype(float) method after converting the cuDF DataFrame into a NumPy array.

Solutions: