NVIDIA-Certified-Professional Accelerated Data Science (NCP-ADS) Free Practice Test

Question 1

You are working on a structured dataset of around 10GB and need to perform exploratory data analysis (EDA), feature engineering, and filtering operations efficiently using NVIDIA technologies. The dataset fits into a single GPU's memory.
Which data processing library should you use to achieve the best performance?

A. Spark with RAPIDS Accelerator

B. cuDF

C. pandas

D. Dask DataFrame with Dask-CUDA

Correct Answer: B

Question 2

Which of the following Nvidia technologies is commonly used for deploying machine learning models in production environments, enabling scalable deployment and monitoring?

A. Nvidia Jetson Nano

B. Nvidia CUDA

C. Nvidia Triton Inference Server

D. Nvidia DeepStream

Correct Answer: C

Question 3

You are working on a financial dataset that tracks stock prices over time, and you need to detect anomalies such as sudden spikes or drops using NVIDIA technologies.
Which of the following approaches would be the most effective for anomaly detection in a time-series dataset using NVIDIA's RAPIDS AI and TensorRT?

A. Perform anomaly detection by applying DBSCAN clustering with RAPIDS cuML without any feature engineering.

B. Use RAPIDS cuML's Isolation Forest for anomaly detection and deploy it with NVIDIA Triton Inference Server.

C. Use traditional ARIMA modeling with RAPIDS cuML to classify anomalies based on residual analysis.

D. Apply a traditional rule-based thresholding method using pandas and NumPy for detecting sudden spikes in stock prices.

Correct Answer: B

Question 4

You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A. Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format

B. Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations

C. Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence

D. Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold

Correct Answer: D

Question 5

A machine learning engineer wants to accelerate the training of a large deep learning model while optimizing GPU utilization.
Which of the following strategies provides the best performance improvement using NVIDIA technologies?

A. Use NVIDIA DALI (Data Loading Library) to preprocess and augment data on the GPU before feeding it to the model.

B. Disable mixed-precision training since it reduces numerical accuracy and does not significantly impact performance.

C. Use CPU-based data augmentation techniques before transferring the data to the GPU for training.

D. Load all training data into GPU memory at once to minimize CPU-GPU data transfer time.

Correct Answer: A

Question 6

You are tasked with profiling a deep learning model using NVIDIA's DLProf to identify performance bottlenecks and optimize resource utilization.
Which of the following statements correctly describes the capabilities of DLProf?

A. DLProf requires significant modifications to the source code to collect profiling data.

B. DLProf is primarily designed for debugging model accuracy rather than performance analysis.

C. DLProf only works with TensorFlow models and does not support PyTorch-based workloads.

D. DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.

Correct Answer: D

Question 7

You need to determine the optimal data processing library for a small dataset of 500,000 records that will be processed on a multi-core CPU machine with no GPU access.
Which of the following libraries would be the most efficient for this task?

A. cuDF

B. pandas

C. Dask

D. PyTorch

Correct Answer: B

Question 8

Which of the following is the best approach for performing benchmarking and optimizing GPU- accelerated workflows for MLOps using Nvidia technologies?

A. Use TensorRT to optimize deep learning models by converting them into highly optimized inference engines, allowing faster execution with lower latency.

B. Use Nvidia's nsight tools to benchmark only the model training phase and ignore the inference phase, as training is the primary bottleneck.

C. Rely exclusively on the nvidia-smi tool for monitoring GPU utilization and memory usage across multiple GPUs without making any other performance adjustments.

D. Use Nvidia's nvprof tool to profile GPU resource usage and identify bottlenecks, then adjust the batch size to optimize throughput.

Correct Answer: D

Question 9

A data scientist is preparing a dataset containing numerical features with varying scales and distributions. Some features range from 0 to 1, while others have values ranging from -5000 to 5000. The dataset will be used in a machine learning model that relies on gradient-based optimization.
What is the best approach to standardizing the data to ensure uniformity across features?

A. Use min-max scaling to transform all features into the range [0,1]

B. Normalize the dataset by dividing each feature by its maximum absolute value

C. Keep the features as they are since machine learning models can handle varying scales naturally

D. Apply z-score normalization (standardization) to scale features to have a mean of 0 and a standard deviation of 1

Correct Answer: D

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NVIDIA-Certified-Professional Accelerated Data Science (NCP-ADS) Free Practice Test