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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric features are standardized for better model performance.
Which of the following is the best approach for scaling data using RAPIDS?

A) df_scaled = (df - df.min()) / (df.max() - df.min())
B) scaler = cuml.preprocessing.StandardScaler()
C) df_scaled = scaler.fit_transform(df)
D) df_scaled = df / df.max()
E) df_scaled = df.apply(lambda x: x / np.linalg.norm(x))

2. You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.
Which of the following approaches best aligns with these goals?

A) Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.
B) Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.
C) Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.
D) Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.

3. A manufacturing company wants to detect anomalies in a real-time IoT sensor dataset using NVIDIA technologies. The data arrives as a continuous stream, and the company needs to process it efficiently for immediate anomaly detection.
Which NVIDIA-powered approach is the most suitable for this use case?

A) Apply cuML's Isolation Forest on incoming streaming data for real-time anomaly detection
B) Leverage cuGraph to model the sensor readings as a graph and detect anomalies using centrality measures
C) Use RAPIDS cuDF with batch processing to analyze historical anomalies every few hours
D) Deploy an Autoencoder-based deep learning model using NVIDIA TensorRT for real-time inference

4. A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A) Using only virtual environments (venv) without managing GPU dependencies separately
B) Using a single system-wide installation of CUDA and forcing all projects to use the same version
C) Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
D) Manually installing each package and its dependencies using pip

5. You are working with structured tabular data in a cloud-based GPU environment.
Your dataset contains the following columns:
Column Name Example Values Data Type Needed
user_id 15432, 98765, 43210 Integer
purchase_amt 12.99, 35.50, 100.75 Floating Point
category 'Books', 'Electronics' Categorical
Which of the following is the most optimal approach to assign data types to these columns to ensure efficient memory usage and computational performance?

A) 1. df['user_id'] = df['user_id'].astype('int64')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
B) 1. df['user_id'] = df['user_id'].astype('int16')
2. df['purchase_amt'] = df['purchase_amt'].astype('float16')
3. df['category'] = df['category'].astype('string')
C) 1. df['user_id'] = df['user_id'].astype('float32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
D) 1. df['user_id'] = df['user_id'].astype('int32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float32')
3. df['category'] = df['category'].astype('category')

Solutions: