NCP-ADS by NVIDIA Actual Free Exam Q&As

Question 1

A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A. Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision. B. Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency. C. Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32. D. Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information. E. Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price. F. Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.

Discussion 0

Correct Answer: C Vote an answer

Question 2

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

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Correct Answer: B Vote an answer

Question 3

A data scientist is processing a dataset that is too large to fit into the memory of a single GPU. They decide to use Dask with cuDF to leverage multiple GPUs for accelerated computation.
Which of the following approaches ensures efficient parallelism when working with dask_cudf?

A. Assign explicit chunks of data to each worker by manually dividing the dataset before passing it to Dask. B. Use Dask futures to asynchronously submit GPU tasks and collect results when completed. C. Convert the dataset into a Dask cuDF dataframe and use persist() to keep it in GPU memory. D. Use a single-threaded scheduler to avoid contention when running on multiple GPUs.

Discussion 0

Correct Answer: C Vote an answer

Question 4

You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A. Convert the dataset to a NumPy array and manually replace missing values with the mean B. Apply a deep learning-based imputation model before moving data to the GPU C. Use fillna() with a fixed value on the GPU using cuDF D. Drop all rows containing missing values using Pandas before transferring data to the GPU

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Correct Answer: C Vote an answer

Question 5

You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?

A. Split the dataset into multiple files based on a logical partitioning key, such as a date column. B. Convert all numeric columns to float64 for higher precision, even if float32 is sufficient. C. Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing. D. Store the entire dataset in a single large file to minimize the number of files in the directory.

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Correct Answer: A Vote an answer

Question 6

Which tools or technologies from NVIDIA are essential for implementing an efficient MLOps pipeline in production environments? (Select two)

A. NVIDIA CUDA for model training in cloud environments B. NVIDIA Triton Inference Server for managing deployment and serving models C. NVIDIA DLA (Deep Learning Accelerator) for model deployment D. NVIDIA NGC for storing and sharing machine learning datasets E. NVIDIA TensorRT for efficient model inference

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Correct Answer: B,E Vote an answer

Question 7

You need to benchmark GPU-accelerated data science frameworks across both cloud-based and on-premise GPU setups.
Which of the following is the most effective strategy for ensuring consistent and reliable benchmarking results?

A. Running each benchmark multiple times and averaging the results to account for variance in execution time. B. Running benchmarks in isolated virtual machines to simulate real-world multi-tenant cloud environments. C. Prioritizing synthetic benchmarks over real-world workloads to get a more controlled performance measurement. D. Using mixed cloud and on-premise GPU instances to test various hardware types and configurations.

Discussion 0

Correct Answer: A Vote an answer

Question 8

A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A. Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows. B. Manually generate random values using Python's built-in random module and load them into a cuDF DataFrame. C. Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset. D. Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.

Discussion 0

Correct Answer: C Vote an answer

Question 9

A data scientist is working with a large dataset containing missing values and outliers. The dataset will be used for training a machine learning model. The scientist decides to preprocess the data using RAPIDS cuDF, an accelerated dataframe library.
Which of the following is the most efficient approach to handle missing values while maintaining data integrity?

A. Convert missing values to a separate categorical class using df.fillna("missing"). B. Use df.dropna() to remove all rows with missing values. C. Use df.fillna(df.mean()) to replace missing values with the column mean. D. Replace missing values with zero using df.fillna(0).

Discussion 0

Correct Answer: C Vote an answer

Question 10

A data scientist needs to process a dataset containing 10 million records, performing transformations and exploratory data analysis (EDA). The processing needs to be efficient but does not require high- performance multi-GPU execution.
Which of the following libraries provides the best balance between usability and performance?

A. Dask DataFrame, since it automatically parallelizes computations even when the dataset fits in memory. B. Pandas, as it provides a simple API and works well for datasets that fit within system memory. C. cuDF, since GPU acceleration will still provide a speedup even for moderately sized datasets. D. Spark DataFrame, as it is optimized for distributed processing and scales well even for 10 million records.

Discussion 0

Correct Answer: B Vote an answer

Question 11

You are managing a data processing pipeline that utilizes NVIDIA RAPIDS on GPUs for accelerated data transformations. During execution, you notice that the pipeline is not achieving expected performance gains.
What is the most effective approach to monitor and diagnose bottlenecks in this pipeline using NVIDIA technologies?

A. Reduce the dataset size and rerun the pipeline without profiling tools to check for performance improvements. B. Run the pipeline on CPU instead of GPU to compare execution times. C. Enable RAPIDS memory pool logging to check for memory fragmentation and out-of-memory errors. D. Use NVIDIA Nsight Systems to profile kernel execution times and memory transfers.

Discussion 0

Correct Answer: D Vote an answer

Question 12

A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.
Which of the following approaches would best help identify the source of the bottleneck?

A. Disable GPU parallelism and run computations on the CPU to compare performance. B. Use nsys (NVIDIA Nsight Systems) to profile GPU kernel execution and memory transfer times. C. Increase the batch size of data being processed without analyzing memory utilization. D. Rely on nvidia-smi alone to check GPU utilization without considering other profiling tools.

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Correct Answer: B Vote an answer

Question 13

A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?

A. Ignore preprocessing and focus only on model training speed when comparing performance. B. Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys). C. Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations. D. Run all benchmarks on a CPU to ensure fairness across frameworks.

Discussion 0

Correct Answer: B Vote an answer

Question 14

You are working with a large dataset containing 500 million records stored as a parquet file. Your task is to perform data filtering, aggregation, and transformation as efficiently as possible.
Which of the following approaches would be the best choice for accelerated data manipulation using NVIDIA technologies?

A. Convert the dataset to a NumPy array and use standard Python loops B. Use multiprocessing in Python to parallelize pandas operations across CPU cores C. Load the dataset into pandas and use apply() for transformations D. Use RAPIDS cuDF to load, filter, and transform the dataset on the GPU

Discussion 0

Correct Answer: D Vote an answer

NVIDIA-Certified-Professional Accelerated Data Science - NCP-ADS Exam Practice Test