Die neuesten NCP-ADS echte Prüfungsfragen, NVIDIA NCP-ADS originale fragen

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Performance benchmarking and optimization 3. Memory and capacity evaluation
Topic 2: 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
Topic 3: 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 4: 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 5: Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Memory optimization techniques (mixed precision, batching) 3. Multi-GPU training comparison 4. Feature engineering
Topic 6: 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

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Echte NCP-ADS Fragen und Antworten der NCP-ADS Zertifizierungsprüfung

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NVIDIA-Certified-Professional Accelerated Data Science NCP-ADS Prüfungsfragen mit Lösungen (Q60-Q65):

60. Frage
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. Perform all analysis on the CPU to avoid potential GPU memory limitations.
C. Downsample the dataset and analyze a subset using Pandas for efficiency.
D. Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.

Antwort: A

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

Antwort: D

62. Frage
A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A. Compiling all dependencies into the host machine and using system-wide installations
B. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
C. Running the model in a local Python virtual environment and copying dependencies manually
D. Using Docker without GPU support and relying on CPU fallback when running TensorFlow

Antwort: B

63. Frage
You are tasked with comparing the performance of different GPU-accelerated frameworks for a deep learning model. The frameworks you are considering are TensorFlow, PyTorch, and CUDA. To evaluate the performance, you decide to implement a benchmark that measures GPU efficiency, memory usage, and speed.
Which of the following actions should you take to design an effective benchmark? (Select two)

A. Benchmark only the training phase of the deep learning model.
B. Use CPU-based implementations of the same frameworks for comparison.
C. Use a batch size that is optimal for each framework's memory management.
D. Use a common dataset for all frameworks to ensure comparability.
E. Measure GPU utilization and memory usage, but ignore the network and disk I/O.

Antwort: C,D

64. Frage
A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A. Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.
B. Storing and retrieving models from a centralized object storage system.
C. Model monitoring by logging metadata and performance metrics in a database.
D. Training and inference workloads using deep learning models with TensorFlow or PyTorch.

Antwort: D

65. Frage
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