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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with designing an ETL workflow for a large-scale data processing pipeline using NVIDIA technologies. You need to ensure that the extraction, transformation, and loading phases are optimized for performance using hardware acceleration.
Which of the following NVIDIA technologies would be most suitable for accelerating the ETL process?

A) CUDA
B) TensorRT
C) RAPIDS
D) TensorFlow

2. 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) Convert cuDF DataFrames to Pandas DataFrames before performing transformations for compatibility
C) Load all data into a single, large cuDF DataFrame before performing transformations
D) Use cuDF to read and process the data in batches, leveraging Dask-cuDF for distributed computation when necessary

3. You are working with a large dataset using NVIDIA RAPIDS cuDF and need to normalize a numerical column (price) to scale its values between 0 and 1.
Which of the following approaches correctly normalizes the column using cuDF?

A) df["price"] = df["price"].applymap( 2. lambda x: (x - df["price"].min()) 3. / (df["price"].max() - df["price"].min()) 4. )
B) df["price"] = df["price"] / df["price"].max()
C) df["price"] = (df["price"] - df["price"].mean()) / df["price"].std()
D) df["price"] = ( 2. df["price"] - df["price"].min() 3. ) / (df["price"].max() - df["price"].min())

4. You are conducting rapid experimentation on an NVIDIA GPU to determine the best trade-off between model accuracy and inference latency.
Which approach is the most efficient for systematically evaluating multiple configurations?

A) Use automated hyperparameter tuning tools like Optuna or Ray Tune with mixed precision training
B) Test different model configurations on a CPU first before moving to the GPU for final evaluation
C) Reduce training epochs significantly to save time, even if the model is underfitting
D) Train each possible model variation from scratch to evaluate accuracy and performance differences

5. You need to deploy a containerized machine learning model that utilizes NVIDIA GPUs on a cloud- based Kubernetes cluster.
Which of the following steps is essential for ensuring proper GPU utilization inside a Docker container?

A) Install GPU drivers inside the container to ensure access to the host's hardware
B) Use the NVIDIA Container Toolkit (nvidia-docker2) to allow GPU access within Docker containers
C) Use a standard Python-based container image instead of an NVIDIA GPU-optimized image
D) Run the container using the default Docker runtime without any additional configurations

Solutions: