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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

A. Use NumPy's statistical functions, such as numpy.mean() and numpy.var()
B. Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods
C. Use a traditional SQL database to compute statistics and then transfer results to the GPU
D. Use cuDF's built-in statistical functions like .mean(), .median(), and .var()

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

Question 3

A machine learning engineer is tasked with optimizing an image classification model on a cloud platform. The engineer must select a GPU-accelerated instance that balances cost and performance while ensuring compatibility with frameworks like TensorFlow and PyTorch.
Which instance configuration is the most appropriate choice?

A. A single-core CPU instance with high disk I/O throughput for faster data loading.
B. A CPU-only instance with 128 GB of RAM for increased data processing speed.
C. A cloud instance with integrated graphics rather than dedicated NVIDIA GPUs.
D. A cloud instance with NVIDIA A100 GPUs and NVLink support.

Question 4

Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

A. Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
B. Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.
C. Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.
D. Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.

Question 5

You are working with a large dataset in RAPIDS cuDF and plan to standardize the numerical features using cuml.preprocessing.StandardScaler(). However, some columns contain missing values.
What is the best approach to handle the missing values before applying standardization?

A. cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
B. Replace missing values with zero before standardization.
C. Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.
D. Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.

Solutions: