NCP-ADS Fragen&Antworten, NCP-ADS Zertifizierungsantworten

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA NCP-ADS Prüfung Übungen und Antworten

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NVIDIA-Certified-Professional Accelerated Data Science NCP-ADS Prüfungsfragen mit Lösungen (Q41-Q46):

41. Frage
You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

A. NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.
B. NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
C. NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
D. NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.

Antwort: B

42. Frage
You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A. Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
B. Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.
C. Perform all EDA using NumPy and SciPy for optimized array computations.
D. Use Pandas directly for data manipulation and visualization.

Antwort: B

43. Frage
A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?

A. Ignore preprocessing and focus only on model training speed when comparing performance.
B. Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations.
C. Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys).
D. Run all benchmarks on a CPU to ensure fairness across frameworks.

Antwort: C

44. Frage
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. Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.
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. Use pandas.to_numeric() since pandas automatically handles type conversion.

Antwort: B

45. Frage
Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A. Min-Max Scaling
B. Z-score Standardization
C. Log Transformation
D. Robust Scaling

Antwort: D

46. Frage
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