Authorized NCP-ADS Certification, Questions NCP-ADS Exam

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
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. Performance benchmarking and optimization 2. Memory and capacity evaluation 3. Model deployment in production environments
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)
Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. Data normalization and standardization 3. cuDF and pandas data preprocessing
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Use cuGraph for graph analytics 2. Perform time series analysis and visualization 3. Detect anomalies in time series datasets
Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Multi-GPU training comparison 3. Hyperparameter tuning 4. Feature engineering

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q217-Q222):

NEW QUESTION # 217
Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A. Robust Scaling
B. Min-Max Scaling
C. Log Transformation
D. Z-score Standardization

Answer: A

NEW QUESTION # 218
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. Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.
B. Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).
C. Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.
D. Switch from stochastic gradient descent (SGD) to Adam optimizer, as Adam improves convergence and computational efficiency.

Answer: B

NEW QUESTION # 219
You have trained a machine learning model and are deploying it into production in a cloud-based environment. You need to ensure the model is continuously monitored for performance, including drift in input data and prediction quality.
Which of the following techniques should you use to effectively monitor the model in production? (Select two)

A. Set up continuous model retraining pipelines triggered by data drift or performance degradation.
B. Restrict access to the model's API to minimize the number of calls and reduce monitoring overhead.
C. Use manual inspection of model predictions periodically to assess accuracy in production.
D. Implement model versioning and rollback procedures to maintain consistency in production models.

Answer: A,D

NEW QUESTION # 220
You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?

A. Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.
B. Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.
C. Use cuDF to manipulate data distributions and generate new data points based on existing features.

Answer: C

NEW QUESTION # 221
You are working on optimizing a deep learning model for inference on an NVIDIA GPU. You decide to use NVIDIA DLProf to profile the model and analyze its performance. After running DLProf, you review the generated reports and find that the GPU Utilization is significantly lower than expected.
Which of the following is the most likely reason for this issue, as indicated by the profiling data?

A. The model contains a large number of small, inefficient kernel launches that introduce overhead.
B. DLProf detected a high level of tensor core utilization, which generally indicates poor performance.
C. The GPU lacks sufficient VRAM, causing frequent memory swaps to system RAM.
D. The batch size is too large, leading to excessive memory allocation failures.

Answer: A

NEW QUESTION # 222
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