NVIDIA-Certified-Professional Accelerated Data Science - NCP-ADS Free Exam Questions

A. Overclock the GPU to achieve higher computational speeds and improve training times.

B. Optimize GPU performance by limiting the number of threads running on each GPU.

C. Use a single cloud instance with the largest GPU available to ensure maximum performance.

D. Use larger batch sizes to make better use of GPU memory during model training.

E. Utilize multi-GPU training to parallelize the workload, reducing training time.

A. Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.

B. Run each model on the same GPU type and instance size across both cloud platforms.

C. Measure both GPU utilization and network latency between the cloud platform and your data storage location.

D. Limit the benchmarking tests to a single model architecture for each cloud platform.

A. MLOps ensures that models trained on GPUs can only run on GPUs during deployment

B. MLOps replaces the need for data preprocessing during training and deployment

C. MLOps frameworks support version control and automation, ensuring reproducibility and scalability of ML workflows

D. MLOps helps manage the lifecycle of machine learning models, ensuring efficient collaboration and model governance

A. Using NumPy for array computations and parallelizing with Python's multiprocessing

B. Using RAPIDS cuDF to replace pandas operations

C. Using Dask to distribute processing across multiple CPU cores

D. Running data transformations in a multi-threaded CPU environment

A. Acquiring and exploring the dataset to assess quality, completeness, and potential biases.

B. Selecting the most appropriate machine learning algorithm for the prediction task.

C. Deploying the model into a production environment for real-time inference.

D. Identifying and preparing feature engineering strategies to improve model accuracy.

A. Reducing the learning rate

B. Increasing the number of layers in the model

C. Applying dropout regularization

D. Removing data augmentation techniques

A. Pandas, as it provides a simple API and works well for datasets that fit within system memory.

B. Dask DataFrame, since it automatically parallelizes computations even when the dataset fits in memory.

C. Spark DataFrame, as it is optimized for distributed processing and scales well even for 10 million records.

D. cuDF, since GPU acceleration will still provide a speedup even for moderately sized datasets.

A. Use dask_gpu instead of dask_cuda to manage memory automatically across GPUs.

B. Allow Dask to allocate data chunks dynamically without partitioning the dataset first, letting the system handle memory distribution automatically.

C. Ensure that each GPU's memory usage is manually monitored and adjusted, as Dask does not manage memory allocation automatically across GPUs.

D. Split the dataset into smaller partitions that fit into each GPU's memory to prevent out-of-memory errors, and let Dask manage data distribution.

A. df['sales'].apply(lambda x: int(x))

B. df['sales'] = df['sales'].to_numeric('int32')

C. df['sales'].convert_dtypes('int32')

D. df['sales'] = df['sales'].astype('int32')

A. Store all intermediate shuffle data in CPU memory using pandas to ensure persistence and reduce GPU load.

B. Enable CUDA Unified Memory to automatically optimize shuffle performance without manual intervention.

C. Use RAPIDS cuDF's GPU memory caching to store intermediate DataFrames and avoid redundant shuffle operations.

D. Use RAPIDS cuML to replace shuffle-intensive operations with an ML model that predicts data distribution.

A. Use cuDF alone for processing log data, as it provides optimal performance on a single GPU.

B. Use NumPy for data transformations before converting the dataset into cuDF for final storage.

C. Process data using Pandas, then export the results to a CSV file for GPU-accelerated analytics.

D. Use Dask-cuDF to distribute cuDF DataFrame operations across multiple GPUs, enabling parallel ETL processing.

A. df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)

B. df = (df - df.mean()) / df.std()

C. df = (df - df.min()) / (df.max() - df.min())

D. 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)

A. Using quantile() to calculate the interquartile range (IQR) and filter out outliers

B. Using clip() to set a maximum and minimum threshold for numerical values

C. Using applymap() to apply a custom function for handling outliers

D. Using dropna() to remove rows with outliers

A. Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.

B. Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.

C. Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.

D. Implement ETL processes using only SQL-based transformations within a relational database system.

A. Lower energy consumption compared to CPUs

B. Lower cost of cloud infrastructure

C. Efficient handling of matrix operations in machine learning models

D. Better for memory-intensive workloads

E. Faster parallel processing for large datasets