NCP-ADS aktueller Test, Test VCE-Dumps für NVIDIA-Certified-Professional Accelerated Data Science

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science NCP-ADS Prüfungsfragen mit Lösungen (Q277-Q282):

277. Frage
You need to set up an isolated, GPU-accelerated environment for a deep learning project that requires specific CUDA, cuDNN, and RAPIDS versions.
Which of the following best ensures a reproducible environment using Docker?

A. Use a system-wide CUDA installation and mount the /usr/local/cuda directory into the container to provide GPU support.
B. Install NVIDIA drivers manually inside a Docker container every time it runs.
C. Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.
D. Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.

Antwort: D

278. Frage
A data science team is using NVIDIA GPUs to accelerate their AI workflow for a fraud detection system. They follow the CRISP-DM methodology to ensure a structured approach to the project.
During the Data Preparation phase, what is the most effective way to leverage NVIDIA technologies?

A. Use NVIDIA RAPIDS to preprocess large-scale transaction datasets efficiently on GPUs before training the model.
B. Perform all data transformation on a single CPU core to ensure stability before deploying to GPU- accelerated training.
C. Manually clean and transform data using traditional CPU-based processing tools like pandas, then transfer the data to the GPU only for training.
D. Ignore feature engineering and feed raw data directly into the model, relying on deep learning to extract relevant features.

Antwort: A

279. Frage
A data scientist is preprocessing a dataset containing several types of features:
A timestamp column storing millisecond-resolution timestamps.
A column with binary categorical values (Yes/No).
A column containing large continuous numerical values.
A column containing product category codes ranging from 0 to 5000.
Which of the following data type choices is the most optimal for maximizing GPU processing efficiency using NVIDIA cuDF?

A. Use float64 for timestamps, int8 for binary categorical values, float32 for continuous numerical values, and int32 for product category codes.
B. float32 is the best choice for large continuous numerical values, balancing precision and GPU efficiency.
C. Binary categorical values (Yes/No) should be stored as bool, which takes up minimal space.
D. Store timestamps as int64, encode binary values as float16, use float64 for continuous numerical values, and use int8 for product category codes.
E. Convert timestamps to datetime64[ms], encode binary values as bool, use float32 for continuous values, and int16 for product category codes.
F. Use string data type for timestamps, int32 for binary values, float16 for continuous numerical values, and int64 for product category codes.
G. Product category codes (range: 0-5000) fit within int16 (which can hold values from -32,768 to
32,767), making it more memory-efficient than int32.

Antwort: E

280. Frage
You are performing data cleansing on a large dataset using CuDF. The dataset contains numerical values, some of which are outliers. You need to remove or adjust these outliers to make your model training more robust.
Which of the following approaches should you consider for handling outliers efficiently in CuDF? (Select two)

A. Using applymap() to apply a custom function for handling outliers
B. Using dropna() to remove rows with outliers
C. Using quantile() to calculate the interquartile range (IQR) and filter out outliers
D. Using clip() to set a maximum and minimum threshold for numerical values

Antwort: C,D

281. Frage
A team is processing large-scale tabular data using cuDF and cuML on NVIDIA GPUs but is facing performance degradation.
Which of the following techniques would be the most effective in identifying and resolving bottlenecks in the pipeline?

A. Increase GPU clock speed manually to force higher processing power.
B. Reduce the dataset size to a smaller sample to speed up processing.
C. Use nvprof or nsight compute to analyze kernel execution time and memory transfer efficiency.
D. Convert all datasets into pandas DataFrames for initial processing before moving to cuDF.

Antwort: C

282. Frage
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