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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q97-Q102):

NEW QUESTION # 97
A financial analyst is working with an irregularly spaced time-series dataset containing cryptocurrency transactions. The timestamps are not evenly distributed, with some periods having dense data and others having sparse entries. The analyst wants to visualize the data efficiently using GPU acceleration.
What is the best preprocessing approach before visualization?

A. Ignore the irregularity and plot the raw timestamps directly without any preprocessing.
B. Sort the data by timestamp and drop all sparse regions using df.dropna().
C. Resample the time-series to a fixed frequency using cuDF.resample() and fill missing values.
D. Convert the dataset to Pandas and use df.resample() to aggregate by fixed time intervals.

Answer: C

NEW QUESTION # 98
When comparing the required memory with the available memory on a GPU for an MLOps deployment using NVIDIA technologies, which of the following is the best method to optimize memory usage while training large models?

A. Use mixed-precision training to reduce memory requirements by using half-precision floating-point numbers.
B. Use a higher number of GPUs to distribute the model and memory load across the GPUs.
C. Decrease the number of training iterations to reduce memory consumption.
D. Increase the input data size to fully utilize available memory and improve training performance.

Answer: A

NEW QUESTION # 99
You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?

A. Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
B. Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.
C. Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.
D. Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.

Answer: A

NEW QUESTION # 100
You have developed a deep learning model using TensorFlow and trained it on an NVIDIA A100 GPU. The model is deployed in production and serves real-time inference requests. However, the inference latency is high, and you need to optimize performance without retraining the model.
Which of the following approaches is the most effective for optimizing inference performance using NVIDIA technologies?

A. Convert the model to ONNX format and use TensorRT for inference optimization.
B. Implement data augmentation techniques to improve inference efficiency.
C. Enable mixed precision training and retrain the model to improve inference speed.
D. Reduce the batch size to decrease computational overhead and improve latency.

Answer: A

NEW QUESTION # 101
You are working with a large dataset on an NVIDIA GPU, where optimizing memory usage is a priority. Your dataset contains a column, transaction_id, which stores unique integer values ranging between 0 and 100,000.
Which of the following data types is the most memory-efficient choice for this column in cuDF?

A. df['transaction_id'] = df['transaction_id'].astype('int32')
B. df['transaction_id'] = df['transaction_id'].astype('int8')
C. df['transaction_id'] = df['transaction_id'].astype('float32')
D. df['transaction_id'] = df['transaction_id'].astype('int64')

Answer: A

NEW QUESTION # 102
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