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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q170-Q175):

NEW QUESTION # 170
You are analyzing a dataset containing millions of transaction records to detect fraudulent transactions. The dataset is highly imbalanced, with fraudulent cases making up only 0.5% of the data.
Which data analysis strategy will most effectively handle the imbalance while minimizing the risk of overfitting?

A. SMOTE (Synthetic Minority Over-sampling Technique)
B. Stratified random sampling
C. Random undersampling of the majority class
D. Random oversampling of the minority class

Answer: C

NEW QUESTION # 171
You are tasked with processing a large dataset of 100 million records for a deep learning project using NVIDIA technologies. You need to determine the most efficient data processing library for this task to maximize performance and reduce processing time.
Which of the following libraries is best suited for this task?

A. PySpark
B. cuDF
C. pandas
D. Dask

Answer: B

NEW QUESTION # 172
You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?

A. Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.
B. Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
C. Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.
D. Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.

Answer: B

NEW QUESTION # 173
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. Running the model inference on a multi-core CPU server with batch processing enabled.
B. Using an Apache Spark cluster with distributed CPU-based inference.
C. Using a REST API wrapper to load the model dynamically into memory for each request.
D. Deploying the model using NVIDIA Triton Inference Server with TensorRT optimizations.

Answer: D

NEW QUESTION # 174
A data scientist is working with a large dataset that contains string-based numeric values that need to be converted to floating-point numbers for further analysis. The dataset is stored as a cuDF DataFrame, and the scientist needs to ensure the conversion is performed optimally on a GPU.
Which of the following is the best method for converting string-based numeric values to floating-point numbers using NVIDIA-accelerated processing?

A. Use pandas.to_numeric() since pandas automatically handles type conversion.
B. Use cudf.DataFrame.astype(float) to convert string values to floating-point numbers efficiently on a GPU.
C. Use NumPy's astype(float) method after converting the cuDF DataFrame into a NumPy array.
D. Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.

Answer: B

NEW QUESTION # 175
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