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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

A machine learning engineer is working with a financial dataset that contains multiple numerical features, including income, loan amount, and transaction frequency. Some features are normally distributed, while others have a highly skewed distribution with extreme outliers.
Which of the following approaches best ensures uniformity across features before training a model?

A. Scale all numerical features using min-max normalization
B. Remove outliers before applying standardization
C. Use one-hot encoding to transform numerical features into categorical representations
D. Apply log transformation to skewed features before standardizing them with z-score normalization

Question 2

You are working with a large dataset using NVIDIA RAPIDS cuDF and need to normalize a numerical column (price) to scale its values between 0 and 1.
Which of the following approaches correctly normalizes the column using cuDF?

A. df["price"] = df["price"] / df["price"].max()
B. df["price"] = df["price"].applymap( 2. lambda x: (x - df["price"].min()) 3. / (df["price"].max() - df["price"].min()) 4. )
C. df["price"] = ( 2. df["price"] - df["price"].min() 3. ) / (df["price"].max() - df["price"].min())
D. df["price"] = (df["price"] - df["price"].mean()) / df["price"].std()

Question 3

You are working on a large-scale data processing pipeline that involves multi-GPU acceleration using Dask. The dataset is too large to fit into the memory of a single GPU, so you decide to distribute the workload across multiple GPUs using Dask-CUDA.
Which of the following steps is necessary to implement efficient data parallelism across multiple GPUs in a Dask-based workflow?

A. Convert Dask DataFrames into Pandas DataFrames to leverage GPU acceleration through Pandas' built-in multi-threading support.
B. Use dask.dataframe.read_parquet() to load the dataset and let Dask automatically distribute computations across multiple GPUs.
C. Explicitly initialize a LocalCUDACluster with multiple workers, ensuring each worker is assigned a single GPU.
D. Use the DaskExecutor from RAPIDS cuML to offload data processing tasks to distributed GPU workers.

Question 4

When utilizing GPU instances in a cloud environment for data science, which of the following are common considerations? (Select two)

A. Cost-effective GPU instances always provide the best performance
B. GPU performance can be degraded by network latency in distributed training
C. GPUs are suitable for both training and inference tasks but not for preprocessing
D. Cloud providers offer dedicated GPU instances for high-performance needs

Question 5

A data scientist is deploying a deep learning model to production using an NVIDIA GPU-powered inference server. They want to ensure low latency and high throughput while maintaining model accuracy.
Which of the following deployment strategies would be most effective?

A. Disabling mixed-precision inference to avoid accuracy loss
B. Deploying a model without batching to ensure real-time responses
C. Running inference on a CPU instead of a GPU to reduce power consumption
D. Using NVIDIA Triton Inference Server with model ensemble optimization

Solutions: