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

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

• Question 31:

  A machine learning engineering team has a Job with three successive tasks. Each task runs a single notebook. The team has been alerted that the Job has failed in its latest run.

  Which of the following approaches can the team use to identify which task is the cause of the failure?

  A. Run each notebook interactively
  B. Review the matrix view in the Job's runs
  C. Migrate the Job to a Delta Live Tables pipeline
  D. Change each Task's setting to use a dedicated cluster
  B. Review the matrix view in the Job's runs

  ---

  Explanation/Reference:

  To identify which task is causing the failure in the job, the team should review the matrix view in the Job's runs. The matrix view provides a clear and detailed overview of each task's status, allowing the team to quickly identify which task

  failed. This approach ismore efficient than running each notebook interactively, as it provides immediate insights into the job's execution flow and any issues that occurred during the run.

  References:

  Databricks documentation on Jobs: Jobs in Databricks

• Question 32:

  Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

  A. The vectorized pandas UDFs allow for the use of type hints
  B. The vectorized pandas UDFs process data in batches rather than one row at a time
  C. The vectorized pandas UDFs allow for pandas API use inside of the function
  D. The vectorized pandas UDFs work on distributed DataFrames
  E. The vectorized pandas UDFs process data in memory rather than spilling to disk
  B. The vectorized pandas UDFs process data in batches rather than one row at a time

  ---

  Explanation/Reference:

  Vectorized pandas UDFs, also known as Pandas UDFs, are a powerful feature in PySpark that allows for more efficient operations than standard UDFs. They operate by processing data in batches, utilizing vectorized operations that leverage pandas to perform operations on whole batches of data at once. This approach is much moreefficient than processing data row by row as is typical with standard PySpark UDFs, which can significantly speed up the computation. References: PySpark Documentation on UDFs:https://spark.apache.org/docs/latest/api/python/user_guide/sql/arrow_panda s.html#pandas-udfs-a-k-a-vectorized-udfs

• Question 33:

  A machine learning engineer has identified the best run from an MLflow Experiment. They have stored the run ID in the run_id variable and identified the logged model name as "model". They now want to register that model in the MLflow Model Registry with the name "best_model".

  Which lines of code can they use to register the model associated with run_id to the MLflow Model Registry?

  A. mlflow.register_model(run_id, "best_model")
  B. mlflow.register_model(f"runs:/{run_id}/model", "best_model")
  C. millow.register_model(f"runs:/{run_id)/model")
  D. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
  B. mlflow.register_model(f"runs:/{run_id}/model", "best_model")

  ---

  Explanation/Reference:

  To register a model that has been identified by a specific run_id in the MLflow Model Registry, the appropriate line of code is:

  mlflow.register_model(f"runs:/{run_id}/model","best_model") This code correctly specifies the path to the model within the run (runs:/{run_id}/model) and registers it under the name "best_model" in the Model Registry. This allows the model to

  be tracked, managed, and transitioned through different stages (e.g., Staging, Production) within the MLflow ecosystem.

  References:

  MLflow documentation on model registry:

  https://www.mlflow.org/docs/latest/model-registry.html#registering-a-model

• Question 34:

  Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?

  A. F1
  B. R-squared
  C. MAE
  D. MSE
  A. F1

  ---

  Explanation/Reference:

  The code block provided by the machine learning engineer will perform the desired inference when the Feature Store feature set was logged with the model at model_uri. This ensures that all necessary feature transformations and metadata

  are available for the model to make predictions. The Feature Store in Databricks allows for seamless integration of features and models, ensuring that the required features are correctly used during inference.

  References:

  Databricks documentation on Feature Store: Feature Store in Databricks

• Question 35:

  Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

  A. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
  B. pandas API on Spark DataFrames are more performant than Spark DataFrames
  C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
  D. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames
  C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

  ---

  Explanation/Reference:

  The pandas API on Spark DataFrames are made up of Spark DataFrames with additional metadata. The pandas API on Spark aims to provide the pandas-like experience with the scalability and distributed nature of Spark. It allows users to

  work with pandas functions on large datasets by leveraging Spark's underlying capabilities.

  References:

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

• Question 36:

  An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.

  Which of the following explanations justifies this suggestion?

  A. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
  B. One-hot encoding is dependent on the target variable's values which differ for each apaplication.
  C. One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
  D. One-hot encoding is not a common strategy for representing categorical feature variables numerically.
  A. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.

  ---

  Explanation/Reference:

  The suggestion not to one-hot encode categorical feature variables within the feature repository is justified because one-hot encoding can be problematic for some machine learning algorithms. Specifically, one-hot encoding increases the dimensionality of the data, which can be computationally expensive and may lead to issues such as multicollinearity and overfitting. Additionally, some algorithms, such as tree-based methods, can handle categorical variables directly without requiring one-hot encoding. References: Databricks documentation on feature engineering: Feature Engineering

• Question 37:

  A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:

  

  Assuming the default Spark configuration is in place, which of the following is a benefit of using anIterator?

  A. The data will be limited to a single executor preventing the model from being loaded multiple times
  B. The model will be limited to a single executor preventing the data from being distributed
  C. The model only needs to be loaded once per executor rather than once per batch during the inference process
  D. The data will be distributed across multiple executors during the inference process
  C. The model only needs to be loaded once per executor rather than once per batch during the inference process

  ---

  Explanation/Reference:

  Using an iterator in thepandas_udfensures that the model only needs to be loaded once per executor rather than once per batch. This approach reduces the overhead associated with repeatedly loading the model during the inference

  process, leading to more efficient and faster predictions. The data will be distributed across multiple executors, but each executor will load the model only once, optimizing the inference process.

  References:

  Databricks documentation on pandas UDFs: Pandas UDFs

• Question 38:

  A data scientist is using Spark SQL to import their data into a machine learning pipeline. Once the data is imported, the data scientist performs machine learning tasks using Spark ML.

  Which of the following compute tools is best suited for this use case?

  A. Single Node cluster
  B. Standard cluster
  C. SQL Warehouse
  D. None of these compute tools support this task
  B. Standard cluster

  ---

  Explanation/Reference:

  For a data scientist using Spark SQL to import data and then performing machine learning tasks using Spark ML, the best-suited compute tool is a Standard cluster. A Standard cluster in Databricks provides the necessary resources and

  scalability to handle large datasets and perform distributed computing tasks efficiently, making it ideal for running Spark SQL and Spark ML operations.

  References:

  Databricks documentation on clusters: Clusters in Databricks

• Question 39:

  Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?

  A. Keras
  B. Scikit-learn
  C. PyTorch
  D. Spark ML
  D. Spark ML

  ---

  Explanation/Reference:

  Spark MLlib is a machine learning library within Apache Spark that provides scalable and distributed machine learning algorithms. It is designed to work with Spark DataFrames and leverages Spark's distributed computing capabilities to perform large-scale feature engineering and model training without the need for user-defined functions (UDFs) or the pandas Function API. Spark MLlib provides built-in transformations and algorithms that can be applied directly to large datasets. References: Databricks documentation on Spark MLlib: Spark MLlib

• Question 40:

  Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?

  A. Keras
  B. pandas
  C. PvTorch
  D. Spark ML
  E. Scikit-learn
  D. Spark ML

  ---

  Explanation/Reference:

  Spark ML (Machine Learning Library) is designed specifically for handling large-scale data processing and machine learning tasks directly within Apache Spark. It provides tools and APIs for large-scale feature engineering without the need to

  rely on user-defined functions (UDFs) or pandas Function API, allowing for more scalable and efficient data transformations directly distributed across a Spark cluster. Unlike Keras, pandas, PyTorch, and scikit-learn, Spark ML operates

  natively in a distributed environment suitable for big data scenarios.References:

  Spark MLlib documentation (Feature Engineering with Spark ML).