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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q94-Q99):

NEW QUESTION # 94
You are tasked with comparing the performance of different GPU-accelerated frameworks for a deep learning model. The frameworks you are considering are TensorFlow, PyTorch, and CUDA. To evaluate the performance, you decide to implement a benchmark that measures GPU efficiency, memory usage, and speed.
Which of the following actions should you take to design an effective benchmark? (Select two)

A. Measure GPU utilization and memory usage, but ignore the network and disk I/O.
B. Benchmark only the training phase of the deep learning model.
C. Use a common dataset for all frameworks to ensure comparability.
D. Use a batch size that is optimal for each framework's memory management.
E. Use CPU-based implementations of the same frameworks for comparison.

Answer: C,D

NEW QUESTION # 95
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. Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.
B. Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.
C. Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
D. Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.

Answer: D

NEW QUESTION # 96
A data scientist is processing a dataset that is too large to fit into the memory of a single GPU. They decide to use Dask with cuDF to leverage multiple GPUs for accelerated computation.
Which of the following approaches ensures efficient parallelism when working with dask_cudf?

A. Use a single-threaded scheduler to avoid contention when running on multiple GPUs.
B. Assign explicit chunks of data to each worker by manually dividing the dataset before passing it to Dask.
C. Use Dask futures to asynchronously submit GPU tasks and collect results when completed.
D. Convert the dataset into a Dask cuDF dataframe and use persist() to keep it in GPU memory.

Answer: D

NEW QUESTION # 97
You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?

A. cuGraph primarily accelerates deep learning tasks and is not optimized for graph-based algorithms.
B. cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.
C. cuGraph enables parallel computation on the GPU, significantly speeding up community detection tasks compared to traditional CPU-based implementations.
D. Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.

Answer: C

NEW QUESTION # 98
You are working with a large dataset in RAPIDS cuDF and plan to standardize the numerical features using cuml.preprocessing.StandardScaler(). However, some columns contain missing values.
What is the best approach to handle the missing values before applying standardization?

A. Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.
B. cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
C. Replace missing values with zero before standardization.
D. Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.

Answer: D

NEW QUESTION # 99
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