NVIDIA NCP-ADS - NVIDIA-Certified-Professional Accelerated Data Science

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A) Using Docker without GPU support and relying on CPU fallback when running TensorFlow
B) Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
C) Running the model in a local Python virtual environment and copying dependencies manually
D) Compiling all dependencies into the host machine and using system-wide installations

2. You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?

A) Use pandas to load the dataset and perform the operations using standard pandas functions
B) Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
C) Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame
D) Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods

3. You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A) Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
B) Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
C) Ensuring the ETL pipeline uses only batch processing for data ingestion.
D) Using a single storage location for both raw and transformed data to simplify the workflow.
E) Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.

4. A retail company is deploying an AI-driven demand forecasting system using NVIDIA GPUs. The team follows the CRISP-DM framework and is currently in the Evaluation phase.
Which approach best leverages NVIDIA technologies to assess model performance effectively?

A) Rely only on training loss as the primary evaluation metric without considering validation performance.
B) Use RAPIDS cuML to rapidly compute evaluation metrics like RMSE and R-squared on large datasets using GPUs.
C) Assume that a high training accuracy guarantees excellent real-world performance, skipping the evaluation phase.
D) Perform evaluation on a small CPU-based subset of the dataset instead of using full GPU-accelerated inference.

5. A machine learning engineer runs NVIDIA DLProf to analyze the performance of a deep learning model and receives a report indicating high GPU idle time.
What is the most likely cause of this issue?

A) The model is experiencing data loading bottlenecks, causing the GPU to wait for input batches.
B) The batch size is too large, leading to excessive GPU memory utilization and slow processing.
C) The GPU is not powerful enough to process the deep learning model efficiently.
D) The CUDA cores are overheating, leading to automatic throttling of computations.

Solutions: