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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. Distributed data processing frameworks (Dask) 3. GPU-accelerated ETL design and implementation
Topic 2: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. CRISP-DM workflow execution 3. Benchmarking GPU workflows
Topic 3: Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Multi-GPU training comparison 3. Memory optimization techniques (mixed precision, batching) 4. Feature engineering
Topic 4: Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Data normalization and standardization 3. Synthetic data generation with RAPIDS
Topic 5: MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Memory and capacity evaluation 3. Model deployment in production environments
Topic 6: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Perform time series analysis and visualization 3. Use cuGraph for graph analytics

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q139-Q144):

NEW QUESTION # 139
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. Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
B. Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.
C. Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.
D. Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.

Answer: D

NEW QUESTION # 140
You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

A. df = (df - df.mean()) / df.std()
B. 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)
C. df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
D. df = (df - df.min()) / (df.max() - df.min())

Answer: B

NEW QUESTION # 141
You are deploying a deep learning model on an edge device with 8GB of available RAM. The model's estimated peak memory usage, including model weights, intermediate tensors, and batch data, is 9.5GB.
What is the best course of action to ensure successful deployment while maintaining performance?

A. Increase the device's swap space to compensate for insufficient RAM
B. Reduce the number of model parameters by removing layers from the architecture
C. Offload some computation to cloud-based processing
D. Reduce the batch size during inference

Answer: D

NEW QUESTION # 142
You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

A. df = cudf.read_csv("data.parquet")
B. df = pd.read_parquet("data.parquet")
C. df = cudf.to_gpu(pd.read_parquet("data.parquet"))
D. df = cudf.read_parquet("data.parquet")

Answer: D

NEW QUESTION # 143
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. Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
B. Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.
C. Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.

Answer: B

NEW QUESTION # 144
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