Databricks DATABRICKS-MACHINE-LEARNING-ASSOCIATE Online Practice Questions and Exam Preparation

Databricks DATABRICKS-MACHINE-LEARNING-ASSOCIATE Online Questions & Answers

• Question 71:

  A data scientist has written a feature engineering notebook that utilizes the pandas library. As the size of the data processed by the notebook increases, the notebook's runtime is drastically increasing, but it is processing slowly as the size of the data included in the process increases.

  Which of the following tools can the data scientist use to spend the least amount of time refactoring their notebook to scale with big data?

  A. PySpark DataFrame API
  B. pandas API on Spark
  C. Spark SQL
  D. Feature Store
  B. pandas API on Spark

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  Explanation/Reference:

  The pandas API on Spark provides a way to scale pandas operations to big data while minimizing the need for refactoring existing pandas code. It allows users to run pandas operations on Spark DataFrames, leveraging Spark's distributed

  computing capabilities to handle large datasets more efficiently. This approach requires minimal changes to the existing code, making it a convenient option for scaling pandas-based feature engineering notebooks.

  References:

  Databricks documentation on pandas API on Spark: pandas API on Spark

• Question 72:

  A machine learning engineer is trying to scale a machine learning pipeline by distributing its single-node model tuning process. After broadcasting the entire training data onto each core, each core in the cluster can train one model at a time. Because the tuning process is still running slowly, the engineer wants to increase the level of parallelism from 4 cores to 8 cores to speed up the tuning process. Unfortunately, the total memory in the cluster cannot be increased.

  In which of the following scenarios will increasing the level of parallelism from 4 to 8 speed up the tuning process?

  A. When the tuning process in randomized
  B. When the entire data can fit on each core
  C. When the model is unable to be parallelized
  D. When the data is particularly long in shape
  E. When the data is particularly wide in shape
  B. When the entire data can fit on each core

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  Explanation/Reference:

  Increasing the level of parallelism from 4 to 8 cores can speed up the tuning process if each core can handle the entire dataset. This ensures that each core can independently work on training a model without running into memory constraints.

  If the entire dataset fits into the memory of each core, adding more cores will allow more models to be trained in parallel, thus speeding up the process.

  References:

  Parallel Computing Concepts

• Question 73:

  Which of the following hyperparameter optimization methods automatically makes informed selections of hyperparameter values based on previous trials for each iterative model evaluation?

  A. Random Search
  B. Halving Random Search
  C. Tree of Parzen Estimators
  D. Grid Search
  C. Tree of Parzen Estimators

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  Explanation/Reference:

  Tree of Parzen Estimators (TPE) is a sequential model-based optimization algorithm that selects hyperparameter values based on the outcomes of previous trials. It models the probability density of good and bad hyperparameter values and

  makes informed decisions about which hyperparameters to try next. This approach contrasts with methods like random search and grid search, which do not use information from previous trials to guide the search process.

  References:

  Hyperopt and TPE

• Question 74:

  A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process.

  Which change could the data scientist make to improve their model accuracy over the course of their tuning process?

  A. Change the number of compute nodes to be half or less than half of the number of evaluations.
  B. Change the number of compute nodes and the number of evaluations to be much larger but equal.
  C. Change the iterative optimization algorithm used to facilitate the tuning process.
  D. Change the number of compute nodes to be double or more than double the number of evaluations.
  C. Change the iterative optimization algorithm used to facilitate the tuning process.

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  Explanation/Reference:

  The lack of improvement in model accuracy across evaluations suggests that the optimization algorithm might not be effectively exploring the hyperparameter space. Iterative optimization algorithms like Tree-structured Parzen Estimators

  (TPE) or Bayesian Optimization can adapt based on previous evaluations, guiding the search towards more promising regions of the hyperparameter space.

  Changing the optimization algorithm can lead to better utilization of the information gathered during each evaluation, potentially improving the overall accuracy.

  References:

  Hyperparameter Optimization with Hyperopt