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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation	17%	- Data validation and quality assurance - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization
Data Analysis	14%	- Exploratory Data Analysis (EDA) - Data visualization and graph analytics - Time-series analysis and anomaly detection - Distributed and parallel data processing
GPU and Cloud Computing	16%	- Resource management and scaling strategies - CRISP-DM and data science methodology - GPU architecture and acceleration principles - Cloud GPU environments and deployment
MLOps	19%	- Monitoring, logging and maintenance - End-to-end workflow management - Model deployment and serving - Pipeline automation and orchestration
Machine Learning	15%	- Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation - Model training and hyperparameter tuning
Data Manipulation and Software Literacy	19%	- Performance profiling and optimization tools - Dependency management and containerization - GPU-accelerated ETL workflows - Data processing libraries selection and usage

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q214-Q219):

NEW QUESTION # 214
You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?

A. Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.
B. Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
C. Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
D. Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.

Answer: C

NEW QUESTION # 215
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. Apply a deep learning-based imputation model before moving data to the GPU
B. Drop all rows containing missing values using Pandas before transferring data to the GPU
C. Use fillna() with a fixed value on the GPU using cuDF
D. Convert the dataset to a NumPy array and manually replace missing values with the mean

Answer: C

NEW QUESTION # 216
You are working on an MLOps workflow that loads a dataset into GPU memory for model training using RAPIDS cuDF. Before performing transformations, you want to verify that the dataset will fit into available GPU memory.
Which of the following methods provides the most accurate estimate of dataset memory consumption in a RAPIDS cudf.DataFrame?

A. cudf_df.memory_usage(deep=True).sum()
B. cudf_df.to_pandas().memory_usage(deep=True).sum()
C. cudf_df.memory_usage().sum()
D. cudf_df.__sizeof__()

Answer: C

NEW QUESTION # 217
You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?

A. Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
B. Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.
C. Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.
D. Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.

Answer: B

NEW QUESTION # 218
You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.
Which of the following steps should you consider to effectively benchmark and optimize GPU- accelerated workflows? (Select two)

A. Optimize data loading by using data augmentation techniques during training to reduce the time spent on I/O operations.
B. Use a dynamic batch size strategy that adjusts the batch size based on available GPU memory to maximize throughput.
C. Use profiling tools to measure the GPU utilization and memory usage during training to identify performance bottlenecks.
D. Increase the batch size and learning rate simultaneously to maximize GPU usage and reduce training time.

Answer: B,C

NEW QUESTION # 219
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