NCP-ADS by NVIDIA Valid Free Exam Practice Test

Question 1

You are working on a large dataset for a machine learning model and need to preprocess the data efficiently using NVIDIA RAPIDS cuDF on a GPU-accelerated system.
Which of the following statements is correct regarding data preparation using cuDF?

A. cuDF can be used as a direct replacement for Pandas, but it is optimized for multi-node distributed computing rather than GPU acceleration. B. cuDF supports common Pandas-like operations, such as filtering, merging, and groupby, but performs them efficiently on the GPU. C. cuDF operations are executed lazily and require an explicit compute() call to execute transformations. D. cuDF requires all input data to be stored in Apache Parquet format, as it does not support CSV or JSON ingestion.

Correct Answer: B

Question 2

A data scientist is working with a dataset of sensor readings (temperature, pressure, vibration) in different scales and units. To ensure all features contribute equally to a machine learning model, the data needs to be standardized.
Which approach is best for standardizing numerical features?

A. Use Min-Max scaling to transform values into a fixed range (e.g., [0,1] or [-1,1]). B. Apply log transformation to all numerical columns to force them into a uniform distribution. C. Apply z-score normalization (standardization) to scale values based on mean and standard deviation. D. Convert all numerical features to categorical values using binning.

Correct Answer: C

Question 3

Which of the following techniques are commonly used to identify and acquire datasets for machine learning and data science projects? (Select three)

A. Web scraping B. Manual data entry C. Data augmentation D. Crowdsourcing E. Public APIs

Correct Answer: A,D,E

Question 4

You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A. Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage. B. Store all data as CSV files and perform ETL operations using traditional row-based processing. C. Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics. D. Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.

Correct Answer: A

Question 5

A data scientist is training a deep learning model and wants to find the best learning rate to optimize convergence speed and generalization. The scientist tests different values: A very small learning rate (0.00001) results in slow convergence.
A very large learning rate (10) causes the model loss to fluctuate wildly and not converge.
Which of the following strategies is the most effective way to optimize the learning rate dynamically during training?

A. Use learning rate warm-up followed by decay B. Use a fixed learning rate chosen through trial and error C. Decrease the learning rate to zero at the end of training (learning rate scheduling) D. Use the same learning rate for all layers in a deep neural network

Correct Answer: A

Question 6

You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A. Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values. B. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values. C. Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration. D. Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.

Correct Answer: C

Question 7

You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

A. Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication. B. Store data in HDFS before performing shuffle operations to reduce GPU memory overhead. C. Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations. D. Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.

Correct Answer: A

Question 8

You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A. Pandas with multiprocessing B. Apache Spark with its default CPU-based execution C. Hadoop MapReduce D. RAPIDS + Dask for distributed GPU-accelerated ETL

Correct Answer: D

Question 9

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

Correct Answer: A

Question 10

You have trained a machine learning model and are deploying it into production in a cloud-based environment. You need to ensure the model is continuously monitored for performance, including drift in input data and prediction quality.
Which of the following techniques should you use to effectively monitor the model in production? (Select two)

A. Implement model versioning and rollback procedures to maintain consistency in production models. B. Use manual inspection of model predictions periodically to assess accuracy in production. C. Set up continuous model retraining pipelines triggered by data drift or performance degradation. D. Restrict access to the model's API to minimize the number of calls and reduce monitoring overhead.

Correct Answer: A,C

Question 11

You are processing a large dataset with UNIX timestamps (seconds since Jan 1, 1970) ranging from Jan 1, 2000, to the present.
What is the most memory-efficient data type for the timestamp column in a GPU-accelerated cloud environment?

A. df['timestamp'] = df['timestamp'].astype('float32') B. df['timestamp'] = df['timestamp'].astype('int64') C. df['timestamp'] = df['timestamp'].astype('datetime64[ms]') D. df['timestamp'] = df['timestamp'].astype('int32')

Correct Answer: D

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