NVIDIA NCP-ADS Test Passed : NVIDIA-Certified-Professional Accelerated Data Science

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

You are deploying a deep learning model on an edge device with 8GB of available RAM. The model's estimated peak memory usage, including model weights, intermediate tensors, and batch data, is 9.5GB.
What is the best course of action to ensure successful deployment while maintaining performance?

A. Reduce the batch size during inference
B. Offload some computation to cloud-based processing
C. Increase the device's swap space to compensate for insufficient RAM
D. Reduce the number of model parameters by removing layers from the architecture

Question 2

You are working on a machine learning dataset with millions of rows and want to optimize performance on a GPU using CuDF. One of the features in your dataset represents categorical data with a limited number of unique values.
Which of the following data types should you use to maximize performance while reducing memory usage?

A. bool
B. int64
C. object (string)
D. category

Question 3

You are working with a large dataset containing missing values, and you need to clean and preprocess the data efficiently.
Which of the following methods provides the best performance when handling missing values in a GPU- accelerated EDA workflow using RAPIDS?

A. Convert the dataset to a cuDF DataFrame and use cudf.DataFrame.fillna() to fill missing values.
B. Use pandas.DataFrame.fillna() on the dataset before converting it to a cuDF DataFrame.
C. Ignore missing values since GPU acceleration can handle incomplete data without performance degradation.
D. Drop all missing values using df.dropna() in Pandas before using RAPIDS.

Question 4

A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

A. Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.
B. Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
C. Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
D. Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.

Question 5

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. Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.
C. Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
D. Use Pandas directly for data manipulation and visualization.

Solutions: