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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q228-Q233):

NEW QUESTION # 228
You are training a machine learning model using NVIDIA RAPIDS cuML and notice that the training process is significantly slower than expected. You suspect that there are bottlenecks in data movement and computation.
Which of the following techniques can best help you diagnose and resolve these bottlenecks?

A. Move all data from GPU memory to CPU memory before training the model.
B. Use cuml.common.device_auto_mem_size() to check GPU memory usage and adjust batch sizes accordingly.
C. Reduce the number of features used in training without profiling the actual bottlenecks.
D. Use cudf.DataFrame.to_pandas() to convert the dataset to a pandas DataFrame for analysis.

Answer: B

NEW QUESTION # 229
A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

A. Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
B. Downsample the dataset and analyze a subset using Pandas for efficiency.
C. Perform all analysis on the CPU to avoid potential GPU memory limitations.
D. Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.

Answer: A

NEW QUESTION # 230
You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?

A. Run each framework with default settings to compare their out-of-the-box performance without any optimizations.
B. Compare training times only without considering throughput, power efficiency, or memory utilization.
C. Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.
D. Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.

Answer: C

NEW QUESTION # 231
You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

A. Acquiring and exploring the dataset to assess quality, completeness, and potential biases.
B. Deploying the model into a production environment for real-time inference.
C. Identifying and preparing feature engineering strategies to improve model accuracy.
D. Selecting the most appropriate machine learning algorithm for the prediction task.

Answer: A

NEW QUESTION # 232
A financial services company is using NVIDIA RAPIDS cuML to train a credit risk assessment model.
The dataset contains hundreds of numerical and categorical features, including loan amount, credit score, income, employment history, and previous loan defaults.
To optimize feature selection using NVIDIA technologies, which approach should they take?

A. Use cuML's feature selection algorithms, such as Recursive Feature Elimination (RFE), to identify the most important predictors.
B. Keep all features in the dataset to ensure the model captures as much information as possible, even if some features are redundant.
C. Convert all numerical features into categorical variables to simplify the data and reduce the need for feature selection.
D. Avoid feature selection, as modern deep learning models are capable of automatically handling redundant and irrelevant features.

Answer: A

NEW QUESTION # 233
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