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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data engineer is preparing a dataset for training a deep learning model. The dataset contains numerical features with missing values, outliers, and inconsistent units.
Which of the following strategies is the most appropriate for ensuring a standardized and clean dataset?

A) Standardize the dataset using the mean and standard deviation, but keep missing values and outliers unchanged to avoid data manipulation.
B) Remove all rows with missing values and outliers to ensure only clean data is used.
C) Replace missing values with the mean, apply z-score normalization, and clip extreme outliers based on a threshold (e.g., 3 standard deviations).
D) Use the median to fill missing values, convert all numerical values into categorical bins, and apply Min-Max scaling.

2. A data scientist is working on a social network analysis project where they need to find the most influential users in a large-scale graph dataset. The dataset consists of millions of users connected through directed edges.
Which of the following approaches would be the best choice for this task using NVIDIA GPU-accelerated tools?

A) Use cuGraph's bfs() (Breadth-First Search) to find the most influential nodes.
B) Use cuDF's groupby().sum() function to count the number of connections per user.
C) Use cuGraph's pagerank() function to identify influential nodes based on link structure.
D) Convert the graph into a pandas DataFrame and apply NetworkX's PageRank implementation.

3. You are developing an AI model for medical imaging that requires acquiring a large dataset of MRI scans from multiple sources.
Which NVIDIA technology would best assist in acquiring, standardizing, and efficiently handling the dataset?

A) Use NVIDIA Nsight Systems to collect MRI data and visualize potential performance bottlenecks.
B) Use NVIDIA AI Enterprise to directly acquire and store MRI images from multiple hospitals.
C) Use NVIDIA Clara Imaging to standardize, curate, and preprocess MRI datasets for AI model training.
D) Use NVIDIA Morpheus to analyze MRI data for cybersecurity threats before storing it.

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

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) Replace missing values with zero before standardization.
B) cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
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: