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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. Distributed data processing frameworks (Dask) 2. GPU-accelerated ETL design and implementation 3. Data caching and performance optimization
Topic 2: Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. cuDF and pandas data preprocessing 3. Synthetic data generation with RAPIDS
Topic 3: MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Performance benchmarking and optimization 3. Model deployment in production environments
Topic 4: 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 5: Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Feature engineering 3. Memory optimization techniques (mixed precision, batching) 4. Multi-GPU training comparison
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 (Q246-Q251):

NEW QUESTION # 246
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. Using the NVIDIA Spark RAPIDS Accelerator to run Spark workloads on GPUs
B. Switching to Dask and RAPIDS to fully utilize GPU acceleration
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: A

NEW QUESTION # 247
A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.
Which of the following approaches would best help identify the source of the bottleneck?

A. Disable GPU parallelism and run computations on the CPU to compare performance.
B. Use nsys (NVIDIA Nsight Systems) to profile GPU kernel execution and memory transfer times.
C. Increase the batch size of data being processed without analyzing memory utilization.
D. Rely on nvidia-smi alone to check GPU utilization without considering other profiling tools.

Answer: B

NEW QUESTION # 248
After profiling a deep learning model using NVIDIA DLProf, you notice that a specific GEMM (General Matrix Multiplication) operation takes significantly longer than expected. The profiler output reveals that tensor cores are underutilized despite having an Ampere-based GPU with Tensor Cores enabled.
Which of the following actions is the MOST appropriate to improve performance?

A. Switch from stochastic gradient descent (SGD) to Adam optimizer, as Adam improves convergence and computational efficiency.
B. Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.
C. Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).
D. Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.

Answer: C

NEW QUESTION # 249
You are processing a large dataset using NVIDIA Dask-cuDF to distribute GPU-accelerated computation across multiple nodes. Users report inconsistent execution times, with some jobs taking significantly longer than expected.
Which of the following actions would best help diagnose the performance bottleneck?

A. Limit GPU memory usage to force more frequent spilling to disk and observe performance differences.
B. Switch to using Pandas with Dask to compare execution speed differences.
C. Reduce the number of Dask workers to minimize parallel execution overhead.
D. Use Dask's dashboard and NVTX markers to analyze task execution times and GPU utilization.

Answer: D

NEW QUESTION # 250
A data scientist is working with large-scale datasets in a RAPIDS AI pipeline and needs to efficiently process and organize the data while leveraging GPU acceleration.
Which of the following approaches best ensures optimized processing and memory management when using NVIDIA technologies?

A. Process data using Apache Spark on CPU before transferring results to GPU for model training.
B. Store data in a pandas DataFrame first and then convert it to cuDF only when GPU operations are needed.
C. Write intermediate data to disk as CSV files before reading them back into RAPIDS AI to avoid memory overflow.
D. Use cuDF to store and process data in GPU memory, leveraging its vectorized operations for transformations and aggregations.

Answer: D

NEW QUESTION # 251
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