NCP-ADS Mock Exams - NCP-ADS Valid Exam Voucher

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Use cuGraph for graph analytics 2. Detect anomalies in time series datasets 3. Perform time series analysis and visualization
Topic 2: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Memory and capacity evaluation 3. Performance benchmarking and optimization
Topic 3: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Benchmarking GPU workflows 2. CRISP-DM workflow execution 3. Docker and Conda environment management
Topic 4: Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Multi-GPU training comparison 3. Feature engineering 4. Hyperparameter tuning
Topic 5: Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Synthetic data generation with RAPIDS 3. Data normalization and standardization
Topic 6: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. GPU-accelerated ETL design and implementation 3. Data caching and performance optimization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q191-Q196):

NEW QUESTION # 191
Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)

A. Using SQLite as a local database for large-scale data analysis
B. Using Excel to manipulate large datasets
C. Using SQLAlchemy to interact with databases for large data processing
D. Using Dask for parallel processing of large datasets
E. Using Apache Spark for distributed data processing

Answer: D,E

NEW QUESTION # 192
You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.
Which of the following steps should you consider to effectively benchmark and optimize GPU- accelerated workflows? (Select two)

A. Optimize data loading by using data augmentation techniques during training to reduce the time spent on I/O operations.
B. Use profiling tools to measure the GPU utilization and memory usage during training to identify performance bottlenecks.
C. Increase the batch size and learning rate simultaneously to maximize GPU usage and reduce training time.
D. Use a dynamic batch size strategy that adjusts the batch size based on available GPU memory to maximize throughput.

Answer: B,D

NEW QUESTION # 193
A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?

A. Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.
B. Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.
C. Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.
D. Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.

Answer: C

NEW QUESTION # 194
You are working on a large dataset (several terabytes in size) and need to perform data preprocessing, filtering, and transformations before training a machine learning model.
Given the dataset size and the requirement to optimize for GPU acceleration using NVIDIA technologies, which of the following is the most appropriate data processing library to use?

A. NumPy with CuPy acceleration
B. pandas
C. Dask DataFrame with Dask-CUDA
D. Modin with Ray backend

Answer: C

NEW QUESTION # 195
You are working with a dataset in a cloud-based GPU environment that contains a column country representing the country of origin for customers. The column contains only 10 unique country values, but the dataset has millions of rows.
Which of the following is the most memory-efficient approach to handle the country column in a cuDF DataFrame?

A. df['country'] = df['country'].astype('object')
B. df['country'] = df['country'].astype('int32')
C. df['country'] = df['country'].astype('category')
D. df['country'] = df['country'].astype('string')

Answer: C

NEW QUESTION # 196
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