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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A machine learning engineer is working on a dataset with thousands of numerical features. The dataset is too large for standard CPU-based processing, so the engineer decides to leverage GPUs for efficient feature engineering.
Which of the following techniques is the most suitable for dimensionality reduction using GPU acceleration?

A) Applying RAPIDS cuML's PCA (Principal Component Analysis) to reduce feature dimensions efficiently.
B) Encoding all numerical features as categorical variables to reduce dimensionality.
C) Converting the dataset into a sparse matrix and running traditional singular value decomposition (SVD) on a CPU.
D) Using manual feature selection by dropping columns with low variance on a CPU.

2. You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A) df.memory_usage(deep=True).sum()
B) df.info(memory_usage='deep')
C) sys.getsizeof(df)
D) np.array(df).nbytes

3. A data scientist is working with a large dataset that contains string-based numeric values that need to be converted to floating-point numbers for further analysis. The dataset is stored as a cuDF DataFrame, and the scientist needs to ensure the conversion is performed optimally on a GPU.
Which of the following is the best method for converting string-based numeric values to floating-point numbers using NVIDIA-accelerated processing?

A) Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.
B) Use cudf.DataFrame.astype(float) to convert string values to floating-point numbers efficiently on a GPU.
C) Use NumPy's astype(float) method after converting the cuDF DataFrame into a NumPy array.
D) Use pandas.to_numeric() since pandas automatically handles type conversion.

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

5. A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

A) Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
B) Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.
C) Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
D) Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.

Solutions: