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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Data cleaning, preprocessing and transformation - Data validation and quality assurance - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization
Topic 2: MLOps	19%	- End-to-end workflow management - Pipeline automation and orchestration - Monitoring, logging and maintenance - Model deployment and serving
Topic 3: Data Analysis	14%	- Exploratory Data Analysis (EDA) - Distributed and parallel data processing - Time-series analysis and anomaly detection - Data visualization and graph analytics
Topic 4: GPU and Cloud Computing	16%	- GPU architecture and acceleration principles - CRISP-DM and data science methodology - Resource management and scaling strategies - Cloud GPU environments and deployment
Topic 5: Machine Learning	15%	- GPU-accelerated ML frameworks and algorithms - Model evaluation and validation - Model training and hyperparameter tuning - Distributed training strategies
Topic 6: Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Data processing libraries selection and usage - Dependency management and containerization - Performance profiling and optimization tools

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q69-Q74):

NEW QUESTION # 69
When scaling data parallelism using Dask with multiple Nvidia GPUs, what is the key consideration to avoid memory issues when distributing large datasets?

A. Ensure that each GPU's memory usage is manually monitored and adjusted, as Dask does not manage memory allocation automatically across GPUs.
B. Use dask_gpu instead of dask_cuda to manage memory automatically across GPUs.
C. Allow Dask to allocate data chunks dynamically without partitioning the dataset first, letting the system handle memory distribution automatically.
D. Split the dataset into smaller partitions that fit into each GPU's memory to prevent out-of-memory errors, and let Dask manage data distribution.

Answer: D

NEW QUESTION # 70
You have a multi-GPU cluster running RAPIDS with Dask to process a large dataset stored in Apache Parquet format. During execution, you notice some GPUs are underutilized, while others are overloaded, leading to uneven processing times.
What is the most effective way to balance the workload across GPUs?

A. Switch to a CPU-based framework like Spark to distribute the load evenly
B. Use Dask's adaptive scaling to dynamically adjust the number of GPU workers
C. Split the dataset into smaller chunks manually and assign them to GPUs
D. Increase the number of worker threads per GPU manually

Answer: B

NEW QUESTION # 71
You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.
What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

A. Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
B. Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
C. Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
D. Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
E. Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.

Answer: B,D

NEW QUESTION # 72
You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

A. Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
B. Perform the join operation entirely in a CPU-based Spark environment for better stability
C. Convert both DataFrames to Pandas before performing the join for compatibility
D. Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF

Answer: A

NEW QUESTION # 73
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. Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.
B. Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.
C. Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.

Answer: A

NEW QUESTION # 74
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