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

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are designing an ETL pipeline to process terabytes of financial transaction data in real time.
The pipeline consists of:
Extracting data from multiple sources (CSV, Parquet, and SQL databases), Transforming the data using operations such as filtering, joins, and aggregations, Loading the processed data into a data lake for analytics.
Given that you are using NVIDIA RAPIDS cuDF for GPU-accelerated ETL, which of the following approaches optimizes performance while ensuring scalability?

A) Use CPU-based ETL frameworks such as Apache Spark without GPU acceleration
B) Use cuDF to read and process the data in batches, leveraging Dask-cuDF for distributed computation when necessary
C) Convert cuDF DataFrames to Pandas DataFrames before performing transformations for compatibility
D) Load all data into a single, large cuDF DataFrame before performing transformations

2. A data scientist is working with large-scale tabular datasets and wants to optimize data ingestion and storage for accelerated processing on NVIDIA GPUs. The scientist is considering different file formats and storage optimizations to maximize performance in a RAPIDS-based workflow.
Which of the following approaches is the most suitable for optimizing both storage and processing performance?

A) Use JSON format for easy readability and process it directly in cuDF.
B) Store data in Parquet format and load it using cuDF in RAPIDS.
C) Load data directly into NumPy arrays before using RAPIDS cuDF for processing.
D) Convert datasets into CSV format and store them in local disk storage.

3. A data scientist is deploying a deep learning model to production using an NVIDIA GPU-powered inference server. They want to ensure low latency and high throughput while maintaining model accuracy.
Which of the following deployment strategies would be most effective?

A) Running inference on a CPU instead of a GPU to reduce power consumption
B) Deploying a model without batching to ensure real-time responses
C) Disabling mixed-precision inference to avoid accuracy loss
D) Using NVIDIA Triton Inference Server with model ensemble optimization

4. A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?

A) Storing all training data in GPU memory at once
B) Using mixed precision training with automatic loss scaling
C) Increasing batch size without adjusting the optimizer settings
D) Using single-precision (FP32) calculations for better accuracy

5. A data scientist is working with a dataset of sensor readings (temperature, pressure, vibration) in different scales and units. To ensure all features contribute equally to a machine learning model, the data needs to be standardized.
Which approach is best for standardizing numerical features?

A) Use Min-Max scaling to transform values into a fixed range (e.g., [0,1] or [-1,1]).
B) Convert all numerical features to categorical values using binning.
C) Apply log transformation to all numerical columns to force them into a uniform distribution.
D) Apply z-score normalization (standardization) to scale values based on mean and standard deviation.

Solutions: