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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning	15%	- Model Development and Optimization 1. Multi-GPU training comparison 2. Hyperparameter tuning 3. Memory optimization techniques (mixed precision, batching) 4. Feature engineering
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: Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. Data normalization and standardization 3. cuDF and pandas data preprocessing
Topic 4: 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
Topic 5: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Memory and capacity evaluation 3. Performance benchmarking and optimization
Topic 6: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. GPU-accelerated ETL design and implementation 2. Data caching and performance optimization 3. Distributed data processing frameworks (Dask)

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q240-Q245):

NEW QUESTION # 240
You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A. Apache Spark with its default CPU-based execution
B. Pandas with multiprocessing
C. Hadoop MapReduce
D. RAPIDS + Dask for distributed GPU-accelerated ETL

Answer: D

NEW QUESTION # 241
A financial services company is deploying an AI-driven risk assessment model using NVIDIA GPUs on a cloud platform. To optimize resource utilization and cost efficiency, they need to determine the best GPU deployment strategy.
Which of the following is the most effective approach?

A. Run all AI workloads on a single large GPU instance without any partitioning or workload separation.
B. Choose an on-demand cloud instance with an outdated GPU model to reduce costs, even if performance is compromised.
C. Deploy the model using NVIDIAAI Enterprise with MIG (Multi-Instance GPU) to allocate multiple workloads on a single GPU efficiently.
D. Deploy separate full-GPU instances for each workload, even if they have variable compute demands.

Answer: C

NEW QUESTION # 242
When performing benchmarking and optimization for GPU-accelerated workflows, which of the following tools is best suited for analyzing the memory utilization and computational efficiency of deep learning models running on Nvidia GPUs?

A. Nvidia Nsight Compute
B. Nvidia CUDA Profiler
C. Nvidia TensorRT
D. Nvidia Riva

Answer: A

NEW QUESTION # 243
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. df = (df - df.min()) / (df.max() - df.min())
C. df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
D. 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)

Answer: D

NEW QUESTION # 244
You are working on optimizing a deep learning model for inference on an NVIDIA GPU. You decide to use NVIDIA DLProf to profile the model and analyze its performance. After running DLProf, you review the generated reports and find that the GPU Utilization is significantly lower than expected.
Which of the following is the most likely reason for this issue, as indicated by the profiling data?

A. The batch size is too large, leading to excessive memory allocation failures.
B. The model contains a large number of small, inefficient kernel launches that introduce overhead.
C. DLProf detected a high level of tensor core utilization, which generally indicates poor performance.
D. The GPU lacks sufficient VRAM, causing frequent memory swaps to system RAM.

Answer: B

NEW QUESTION # 245
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