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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. GPU-accelerated ETL design and implementation 3. Distributed data processing frameworks (Dask)
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: 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: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. Benchmarking GPU workflows 3. CRISP-DM workflow execution
Topic 6: Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. cuDF and pandas data preprocessing 3. Synthetic data generation with RAPIDS

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The practice exams (desktop and web-based) are customizable, meaning you can set the NVIDIA-Certified-Professional Accelerated Data Science (NCP-ADS) questions and time according to your needs to improve your preparation for the Professional NVIDIA NCP-ADS certification test. You can give multiple practice tests to improve yourself and even access the result of previously given tests from the history to avoid mistakes while taking the NVIDIA-Certified-Professional Accelerated Data Science (NCP-ADS) test. The practice tests have been made according to the latest pattern so you can practice in real NVIDIA-Certified-Professional Accelerated Data Science (NCP-ADS) exam environment and improve yourself daily.

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q261-Q266):

NEW QUESTION # 261
You are building a deep learning model using TensorFlow with cuDNN acceleration on an NVIDIA GPU. Your dataset contains continuous numerical features with vastly different ranges.
What is the best way to standardize the data efficiently to improve model convergence?

A. Use cuml.StandardScaler() from RAPIDS to normalize the dataset before feeding it into the model.
B. Manually compute the feature mean and variance on the CPU and apply the transformation before training.
C. Apply batch normalization layers in the neural network to handle feature scaling dynamically during training.
D. Use cupy.linalg.norm() to normalize each feature vector individually to unit length.

Answer: A

NEW QUESTION # 262
A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A. Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
B. Manually generate random values using Python's built-in random module and load them into a cuDF DataFrame.
C. Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
D. Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.

Answer: D

NEW QUESTION # 263
A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?

A. Storing all training data in GPU memory at once
B. Using mixed precision training with automatic loss scaling
C. Using single-precision (FP32) calculations for better accuracy
D. Increasing batch size without adjusting the optimizer settings

Answer: B

NEW QUESTION # 264
A machine learning engineer is tasked with deploying a real-time image classification model as part of an MLOps pipeline. The model requires low-latency inference and must handle high-throughput requests efficiently.
Which of the following deployment strategies is the most suitable for leveraging GPU acceleration?

A. Deploying the model using NVIDIA Triton Inference Server with TensorRT optimizations.
B. Running the model inference on a multi-core CPU server with batch processing enabled.
C. Using an Apache Spark cluster with distributed CPU-based inference.
D. Using a REST API wrapper to load the model dynamically into memory for each request.

Answer: A

NEW QUESTION # 265
You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.
Which of the following approaches best aligns with these goals?

A. Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.
B. Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.
C. Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.
D. Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.

Answer: B

NEW QUESTION # 266
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