NVIDIA-Certified-Professional Accelerated Data Science - NCP-ADS FREE EXAM DUMPS QUESTIONS & ANSWERS]

Question 1

You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A. Convert the dataset to a NumPy array and manually replace missing values with the mean B. Apply a deep learning-based imputation model before moving data to the GPU C. Drop all rows containing missing values using Pandas before transferring data to the GPU D. Use fillna() with a fixed value on the GPU using cuDF

Discussion 0

Correct Answer: D Vote an answer

Question 2

In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A. Efficient handling of matrix operations in machine learning models B. Lower cost of cloud infrastructure C. Better for memory-intensive workloads D. Lower energy consumption compared to CPUs E. Faster parallel processing for large datasets

Discussion 0

Correct Answer: A,E Vote an answer

Question 3

Which tools or technologies from NVIDIA are essential for implementing an efficient MLOps pipeline in production environments? (Select two)

A. NVIDIA Triton Inference Server for managing deployment and serving models B. NVIDIA DLA (Deep Learning Accelerator) for model deployment C. NVIDIA CUDA for model training in cloud environments D. NVIDIA TensorRT for efficient model inference E. NVIDIA NGC for storing and sharing machine learning datasets

Discussion 0

Correct Answer: A,D Vote an answer

Question 4

You are running a data science project on a cloud environment, where you need to optimize the GPU utilization for real-time data processing tasks.
Which of the following practices should you consider to maximize GPU performance? (Select two)

A. Use data parallelism techniques to split the workload evenly across multiple GPUs. B. Scale the cloud infrastructure vertically by increasing the size of the GPU in use. C. Use NVIDIA CUDA libraries optimized for data science tasks to accelerate computations. D. Prioritize using CPU for computationally expensive tasks and reserve GPU for I/O operations. E. Implement model inference in a batch mode rather than in a real-time streaming mode for better performance.

Discussion 0

Correct Answer: A,C Vote an answer

Question 5

In Python, when working with large datasets using pandas, which of the following methods are best for improving performance and efficiency when applying operations on DataFrames? (Select two)

A. Using vectorized operations (e.g., element-wise arithmetic) B. Using map() function to apply a function element-wise C. Using for loops to apply operations row by row D. Using iterrows() for iterating through DataFrame rows E. Using apply() function over DataFrame rows

Discussion 0

Correct Answer: A,B Vote an answer

Question 6

A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?

A. Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection. B. Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies. C. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection. D. Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.

Discussion 0

Correct Answer: D Vote an answer

Question 7

Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

A. Training dataset bias. B. GPU utilization and memory usage. C. Hyperparameter tuning results. D. Number of model parameters.

Discussion 0

Correct Answer: B Vote an answer

Question 8

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 Triton Inference Server C. Nvidia DeepStream D. Nvidia CUDA

Discussion 0

Correct Answer: B Vote an answer

Question 9

You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A. np.array(df).nbytes B. df.memory_usage(deep=True).sum() C. df.info(memory_usage='deep') D. sys.getsizeof(df)

Discussion 0

Correct Answer: B Vote an answer

Question 10

A financial institution is developing an ETL pipeline to ingest and process large volumes of streaming data from various sources, including stock market feeds, real-time transactions, and economic indicators. The ETL process must be highly efficient to minimize latency while ensuring data integrity.
Which of the following strategies is best suited for implementing a high-performance, GPU-accelerated ETL pipeline?

A. Use Pandas and Python's built-in threading library to handle concurrent data ingestion and transformation. B. Load data directly into an Excel spreadsheet and use VBA macros to clean and transform it. C. Store all streaming data in a PostgreSQL database before performing batch transformations. D. Utilize NVIDIA Morpheus with RAPIDS to preprocess real-time streaming data using GPU acceleration.

Discussion 0

Correct Answer: D Vote an answer

Question 11

Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A. Robust Scaling B. Min-Max Scaling C. Log Transformation D. Z-score Standardization

Discussion 0

Correct Answer: A Vote an answer

Question 12

You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?

A. Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups. B. Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently. C. Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph. D. Use Pandas DataFrames to manage the graph structure and leverage Pandas indexing for efficient queries.

Discussion 0

Correct Answer: B Vote an answer

Question 13

Which of the following best describes a key advantage of using cloud-based GPU instances for machine learning model training?

A. Cloud GPUs provide dynamically scalable resources, allowing users to increase or decrease compute power based on demand without upfront hardware investment. B. Cloud-based GPU instances offer lower latency and better network performance compared to on- premise deployments, regardless of geographical location. C. Cloud GPU instances cannot support containerized workloads, limiting their applicability for MLOps and CI/CD pipelines. D. Cloud GPUs are always more cost-effective than on-premise GPUs, as they do not incur long-term usage costs.

Discussion 0

Correct Answer: A Vote an answer

Question 14

You are tasked with optimizing the performance of a large-scale data science project that involves deep learning models on a cloud infrastructure. Your organization is using GPUs for model training.
Which of the following strategies would be the most effective in optimizing GPU performance for data science tasks? (Select two)

A. Use larger batch sizes to make better use of GPU memory during model training. B. Optimize GPU performance by limiting the number of threads running on each GPU. C. Overclock the GPU to achieve higher computational speeds and improve training times. D. Utilize multi-GPU training to parallelize the workload, reducing training time. E. Use a single cloud instance with the largest GPU available to ensure maximum performance.

Discussion 0

Correct Answer: A,D Vote an answer