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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q125-Q130):

NEW QUESTION # 125
Which of the following statements best describes the role of GPUs in accelerating data science workloads?

A. GPUs are optimized for sequential data processing tasks, making them more efficient than CPUs for database operations.
B. GPUs are only effective for acceleration when used in conjunction with Tensor Processing Units (TPUs), as they cannot train deep learning models independently.
C. GPUs are designed primarily for rendering graphics and have limited utility in machine learning and deep learning applications.
D. GPUs use thousands of smaller cores that can execute many parallel computations simultaneously, making them ideal for large-scale matrix operations.

Answer: D

NEW QUESTION # 126
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. Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
B. Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
C. Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
D. Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
E. Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.

Answer: B,E

NEW QUESTION # 127
You are designing a reproducible benchmark to compare the performance of deep learning models across frameworks like PyTorch and TensorFlow using NVIDIA's A100 GPU.
Which step is most critical in ensuring fair benchmarking conditions?

A. Measuring only forward pass latency to compare inference speed while ignoring backward pass computation.
B. Enabling XLA compiler optimizations only for TensorFlow to enhance its performance.
C. Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.
D. Using a different precision setting for each framework to maximize performance per framework's capabilities.

Answer: C

NEW QUESTION # 128
A data scientist is working with a 50 TB dataset consisting of structured logs from IoT devices. The data needs to be cleaned, transformed, and aggregated before training a machine learning model.
Which of the following frameworks would be the most efficient choice for distributed data processing?

A. Python multiprocessing module
B. Pandas
C. Apache Spark with RAPIDS Accelerator
D. SQLite

Answer: C

NEW QUESTION # 129
Which of the following scenarios are most appropriate for using GPU acceleration when working with large-scale datasets in machine learning? (Select two)

A. Using decision trees for a classification task with a dataset of 1 million rows and 20 features.
B. Training a large-scale natural language processing (NLP) model on text data with billions of words.
C. Performing exploratory data analysis (EDA) on a dataset of 100,000 rows with 10 features.
D. Running a deep learning model for image classification with millions of labeled images.
E. Running a large ensemble of simple models (e.g., random forests) on a dataset of 10 million rows.

Answer: B,D

NEW QUESTION # 130
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