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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q72-Q77):

NEW QUESTION # 72
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.convert_dtypes()
B. df.astype('float16')
C. df['col'].apply(lambda x: np.float16(x))
D. df.to_float16()

Answer: B

NEW QUESTION # 73
When performing benchmarking and optimization for GPU-accelerated workflows, which of the following tools is best suited for analyzing the memory utilization and computational efficiency of deep learning models running on Nvidia GPUs?

A. Nvidia Riva
B. Nvidia Nsight Compute
C. Nvidia CUDA Profiler
D. Nvidia TensorRT

Answer: B

NEW QUESTION # 74
You are working with a large dataset containing customer transactions and want to perform exploratory data analysis (EDA) efficiently. Given the dataset's size, you decide to use NVIDIA RAPIDS to accelerate the process.
Which of the following approaches is the most effective for conducting EDA using NVIDIA technologies?

A. Use TensorFlow and Keras to preprocess the data before performing EDA
B. Load the dataset into Pandas and use Matplotlib for visualization
C. Perform SQL queries on a CPU-based database for initial data analysis before GPU acceleration
D. Use RAPIDS cuDF to perform fast dataframe operations and visualize results with cuXfilter

Answer: D

NEW QUESTION # 75
You are working with a dataset containing billions of records stored in a Parquet file. You need to load this dataset efficiently into an NVIDIA-accelerated RAPIDS environment for feature engineering.
Which of the following is the best approach?

A. Load the Parquet file into Dask and then convert it into a cuDF DataFrame for parallel processing
B. Load the Parquet file directly into a cuDF DataFrame using cudf.read_parquet()
C. Convert the dataset into a CSV format and use cudf.read_csv() to load it into RAPIDS
D. Use pandas.read_parquet() to load the dataset and then convert it to a cuDF DataFrame

Answer: B

NEW QUESTION # 76
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. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
B. Running the model in a local Python virtual environment and copying dependencies manually
C. Using Docker without GPU support and relying on CPU fallback when running TensorFlow
D. Compiling all dependencies into the host machine and using system-wide installations

Answer: A

NEW QUESTION # 77
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