About NVIDIA-Certified-Professional Accelerated Data Science : NCP-ADS Exam Questions

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?

A) Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods
B) Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame
C) Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
D) Use pandas to load the dataset and perform the operations using standard pandas functions

2. You are working with a large dataset containing numeric and categorical features, which will be processed using NVIDIA RAPIDS cuDF for accelerated analytics.
To optimize performance while minimizing memory usage, which data type is the most appropriate for storing a categorical variable with a small number of unique values?

A) int64 - Provides high precision and avoids potential overflow.
B) category - Optimizes storage and computation for categorical data in cuDF.
C) float32 - Reduces memory consumption compared to float64 while maintaining precision.
D) bool - Minimizes memory usage and supports efficient operations for categorical data.

3. You are processing a large dataset using RAPIDS cuDF and Dask-cuDF on an NVIDIA GPU. Your profiling indicates that data transfer times between CPU and GPU are significantly slowing down your pipeline.
What is the most effective way to reduce this bottleneck?

A) Increase the CPU RAM allocation to store more data before transferring to the GPU
B) Use cudf.read_parquet() instead of Pandas to load data directly into GPU memory
C) Convert the dataset into a CSV format before transferring it to the GPU
D) Transfer data in multiple smaller chunks to the GPU instead of larger batches

4. You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?

A) Define the problem statement and collect relevant datasets before training the model.
B) Deploy the model to an edge device using TensorRT for real-time inference.
C) Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
D) Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.

5. You are considering using a multi-GPU setup to accelerate training a large deep learning model.
Which of the following are important factors to consider when deciding whether to use single-GPU or multi-GPU training? (Select two)

A) The success of multi-GPU training depends heavily on the ability to increase the batch size without exceeding memory limits.
B) Multi-GPU setups require proper load balancing and efficient gradient synchronization, as uneven distribution of work can lead to suboptimal performance.
C) The performance of multi-GPU training scales linearly with the number of GPUs, meaning adding more GPUs will always result in a proportional reduction in training time.
D) The increase in training time from using a multi-GPU setup is negligible, as the overhead from communication and synchronization is minimal.

Solutions: