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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q151-Q156):

NEW QUESTION # 151
A data scientist is using NVIDIA RAPIDS cuDF to process a large dataset of customer transactions.
The dataset contains numerical, categorical, and timestamp-based features.
To optimize memory usage and performance on NVIDIA GPUs, which approach should they take when selecting data types?

A. Store all numerical columns as float64 to preserve maximum precision, even if lower precision suffices.
B. Convert categorical variables into cuDF categorical data types and downcast numerical columns to the smallest possible precision without losing information.
C. Convert all timestamp features into object (string) format to maintain readability and ensure compatibility with GPU processing.
D. Avoid downcasting integer columns, as lower-bit integer types (e.g., int8) are not supported in GPU- accelerated computations.

Answer: B

NEW QUESTION # 152
You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

A. Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF
B. Perform the join operation entirely in a CPU-based Spark environment for better stability
C. Convert both DataFrames to Pandas before performing the join for compatibility
D. Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining

Answer: D

NEW QUESTION # 153
A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.
Which of the following approaches is the most efficient for visualizing this time-series data?

A. Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.
B. Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.
C. Use seaborn with a sampled subset of the dataset to generate line plots.
D. Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.

Answer: D

NEW QUESTION # 154
A data scientist is processing a dataset that is too large to fit into the memory of a single GPU. They decide to use Dask with cuDF to leverage multiple GPUs for accelerated computation.
Which of the following approaches ensures efficient parallelism when working with dask_cudf?

A. Convert the dataset into a Dask cuDF dataframe and use persist() to keep it in GPU memory.
B. Use Dask futures to asynchronously submit GPU tasks and collect results when completed.
C. Assign explicit chunks of data to each worker by manually dividing the dataset before passing it to Dask.
D. Use a single-threaded scheduler to avoid contention when running on multiple GPUs.

Answer: A

NEW QUESTION # 155
Your data science team is performing exploratory data analysis (EDA) on a large GPU-accelerated environment using cuDF and Dask-cuDF. During analysis, queries on categorical columns are performing poorly.
Which approach will most effectively improve query performance for categorical data in GPU-accelerated DataFrames?

A. Convert categorical columns to integer codes
B. Increase GPU memory allocation
C. Store data in compressed file formats like Parquet
D. Use CPU-based DataFrames (pandas) instead of GPU-based DataFrames

Answer: A

NEW QUESTION # 156
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