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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning	15%	- Model evaluation and validation - Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning
Topic 2: Data Analysis	14%	- Data visualization and graph analytics - Distributed and parallel data processing - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA)
Topic 3: MLOps	19%	- End-to-end workflow management - Pipeline automation and orchestration - Model deployment and serving - Monitoring, logging and maintenance
Topic 4: Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Performance profiling and optimization tools - Dependency management and containerization - Data processing libraries selection and usage
Topic 5: GPU and Cloud Computing	16%	- CRISP-DM and data science methodology - Cloud GPU environments and deployment - Resource management and scaling strategies - GPU architecture and acceleration principles
Topic 6: Data Preparation	17%	- Feature engineering and data type optimization - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Data validation and quality assurance

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NCP-ADS Studienmaterialien: NVIDIA-Certified-Professional Accelerated Data Science & NCP-ADS Zertifizierungstraining

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NVIDIA-Certified-Professional Accelerated Data Science NCP-ADS Prüfungsfragen mit Lösungen (Q275-Q280):

275. Frage
You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

A. Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.
B. Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.
C. Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.
D. Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.

Antwort: D

276. Frage
You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
Which of the following is the correct and most efficient way to perform this conversion in cuDF?

A. df['sales'] = df['sales'].to_numeric('int32')
B. df['sales'].apply(lambda x: int(x))
C. df['sales'] = df['sales'].astype('int32')
D. df['sales'].convert_dtypes('int32')

Antwort: C

277. Frage
You are working with large datasets in cuDF and have noticed significant performance bottlenecks due to repeated computation and excessive shuffling in your workflow. You want to use data caching to optimize the execution plan and reduce redundant operations.
Which of the following is the best way to implement data caching in cuDF to avoid repeated recomputation and excessive shuffling?

A. Use the .reset_index() method after performing transformations to force materialization of the dataset and cache results.
B. Use the .persist() method on a cuDF DataFrame to store intermediate results in GPU memory, reducing redundant calculations.
C. Use the .to_pandas() method to convert the cuDF DataFrame into a Pandas DataFrame, leveraging CPU caching instead of GPU caching.
D. Store intermediate DataFrames as temporary CSV files on disk and reload them when needed to simulate a cache.

Antwort: B

278. Frage
You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?

A. df['col'].apply(lambda x: np.float16(x))
B. df.to_float16()
C. df.astype('float16')
D. df.convert_dtypes()

Antwort: C

279. Frage
You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.
Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?

A. NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
B. NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
C. NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.
D. NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.

Antwort: D

280. Frage
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