NVIDIA NCP-ADS Practice Questions

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing
Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Feature engineering 3. Hyperparameter tuning 4. Multi-GPU training comparison
MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Model deployment in production environments 3. Performance benchmarking and optimization
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. CRISP-DM workflow execution 3. Benchmarking GPU workflows
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. GPU-accelerated ETL design and implementation 3. Distributed data processing frameworks (Dask)
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Use cuGraph for graph analytics 2. Perform time series analysis and visualization 3. Detect anomalies in time series datasets

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q44-Q49):

NEW QUESTION # 44
You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

A. Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.
B. Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
C. Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.

Answer: A

NEW QUESTION # 45
You are processing a large dataset with UNIX timestamps (seconds since Jan 1, 1970) ranging from Jan 1, 2000, to the present.
What is the most memory-efficient data type for the timestamp column in a GPU-accelerated cloud environment?

A. df['timestamp'] = df['timestamp'].astype('float32')
B. df['timestamp'] = df['timestamp'].astype('int64')
C. df['timestamp'] = df['timestamp'].astype('datetime64[ms]')
D. df['timestamp'] = df['timestamp'].astype('int32')

Answer: D

NEW QUESTION # 46
A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A. Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
B. Using a single system-wide installation of CUDA and forcing all projects to use the same version
C. Using only virtual environments (venv) without managing GPU dependencies separately
D. Manually installing each package and its dependencies using pip

Answer: A

NEW QUESTION # 47
You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

A. Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.
B. Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
C. Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.
D. Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.

Answer: B

NEW QUESTION # 48
You are tasked with optimizing an ETL pipeline that processes petabytes of data daily. Your organization is already using Apache Spark for distributed data processing but is experiencing performance bottlenecks. You need a solution that improves execution speed without requiring extensive code modifications.
Which of the following solutions best meets your needs?

A. Switching to Dask and RAPIDS to fully utilize GPU acceleration
B. Using the NVIDIA Spark RAPIDS Accelerator to run Spark workloads on GPUs
C. Using Apache Arrow to optimize in-memory columnar processing within Spark
D. Rewriting the entire ETL pipeline in CUDA to maximize GPU efficiency

Answer: B

NEW QUESTION # 49
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