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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. 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) Deploy the model to an edge device using TensorRT for real-time inference.
B) Define the problem statement and collect relevant datasets before training the model.
C) Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.
D) Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
2. You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?
A) Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
B) Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
C) Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.
D) Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.
3. 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) Use pandas to load the dataset and perform the operations using standard pandas functions
B) Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods
C) Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
D) Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame
4. A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?
A) Using dynamic batch sizing based on available GPU memory
B) Loading all dataset samples into GPU memory at the start of training
C) Allocating a fixed batch size without monitoring memory usage
D) Using a batch size of 1 to minimize memory usage
5. Which of the following is the best approach for performing benchmarking and optimizing GPU- accelerated workflows for MLOps using Nvidia technologies?
A) Use Nvidia's nsight tools to benchmark only the model training phase and ignore the inference phase, as training is the primary bottleneck.
B) Rely exclusively on the nvidia-smi tool for monitoring GPU utilization and memory usage across multiple GPUs without making any other performance adjustments.
C) Use Nvidia's nvprof tool to profile GPU resource usage and identify bottlenecks, then adjust the batch size to optimize throughput.
D) Use TensorRT to optimize deep learning models by converting them into highly optimized inference engines, allowing faster execution with lower latency.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: C |
