Quiz Databricks - Databricks-Machine-Learning-Associate–Newest Valid Study Plan

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Databricks Databricks-Machine-Learning-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.
Topic 2	Spark ML: It discusses the concepts of Distributed ML. Moreover, this topic covers Spark ML Modeling APIs, Hyperopt, Pandas API, Pandas UDFs, and Function APIs.
Topic 3	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.
Topic 4	ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.
Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

A. Iterative optimization
B. Spark ML cannot distribute linear regression training
C. Singular value decomposition
D. Least-squares method
E. Logistic regression

Answer: A

Explanation:
For large datasets with many variables, Spark ML distributes the training of a linear regression model using iterative optimization methods. Specifically, Spark ML employs algorithms such as Gradient Descent or L-BFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) to iteratively minimize the loss function. These iterative methods are suitable for distributed computing environments and can handle large-scale data efficiently by partitioning the data across nodes in a cluster and performing parallel updates.
Reference:
Spark MLlib Documentation (Linear Regression with Iterative Optimization).

NEW QUESTION # 17
A data scientist has developed a linear regression model using Spark ML and computed the predictions in a Spark DataFrame preds_df with the following schema:
prediction DOUBLE
actual DOUBLE
Which of the following code blocks can be used to compute the root mean-squared-error of the model according to the data in preds_df and assign it to the rmse variable?

Answer: A

Explanation:
To compute the root mean-squared-error (RMSE) of a linear regression model using Spark ML, the RegressionEvaluator class is used. The RegressionEvaluator is specifically designed for regression tasks and can calculate various metrics, including RMSE, based on the columns containing predictions and actual values.
The correct code block to compute RMSE from the preds_df DataFrame is:
regression_evaluator = RegressionEvaluator( predictionCol="prediction", labelCol="actual", metricName="rmse" ) rmse = regression_evaluator.evaluate(preds_df) This code creates an instance of RegressionEvaluator, specifying the prediction and label columns, as well as the metric to be computed ("rmse"). It then evaluates the predictions in preds_df and assigns the resulting RMSE value to the rmse variable.
Options A and B incorrectly use BinaryClassificationEvaluator, which is not suitable for regression tasks. Option D also incorrectly uses BinaryClassificationEvaluator.
Reference:
PySpark ML Documentation

NEW QUESTION # 18
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. PvTorch
C. pandas
D. Scikit-learn
E. Spark ML

Answer: E

Explanation:
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.
Reference:
Spark MLlib documentation (Feature Engineering with Spark ML).

NEW QUESTION # 19
A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.
Which of the following approaches describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?

A. They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating their clusters.
B. They can check the Databricks Runtime ML box when creating their clusters.
C. They can set the runtime-version variable in their Spark session to "ml".
D. They can add a line enabling Databricks Runtime ML in their init script when creating their clusters.

Answer: A

Explanation:
The Databricks Runtime for Machine Learning includes pre-installed packages and libraries essential for machine learning and deep learning, including MLflow. To use it, the machine learning engineer can simply select an appropriate Databricks Runtime ML version from the "Databricks Runtime Version" dropdown menu while creating their cluster. This selection ensures that all necessary machine learning libraries, including MLflow, are pre-installed and ready for use, avoiding the need to manually install them each time.
Reference
Databricks documentation on creating clusters: https://docs.databricks.com/clusters/create.html

NEW QUESTION # 20
A data scientist is wanting to explore summary statistics for Spark DataFrame spark_df. The data scientist wants to see the count, mean, standard deviation, minimum, maximum, and interquartile range (IQR) for each numerical feature.
Which of the following lines of code can the data scientist run to accomplish the task?

A. spark_df.printSchema()
B. spark_df.stats()
C. spark_df.toPandas()
D. spark_df.describe().head()
E. spark_df.summary ()

Answer: E

Explanation:
The summary() function in PySpark's DataFrame API provides descriptive statistics which include count, mean, standard deviation, min, max, and quantiles for numeric columns. Here are the steps on how it can be used:
Import PySpark: Ensure PySpark is installed and correctly configured in the Databricks environment.
Load Data: Load the data into a Spark DataFrame.
Apply Summary: Use spark_df.summary() to generate summary statistics.
View Results: The output from the summary() function includes the statistics specified in the query (count, mean, standard deviation, min, max, and potentially quartiles which approximate the interquartile range).
Reference
PySpark Documentation: https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.summary.html

NEW QUESTION # 21
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QUIZ DATABRICKS - DATABRICKS-MACHINE-LEARNING-ASSOCIATE–NEWEST VALID STUDY PLAN

Quiz Databricks - Databricks-Machine-Learning-Associate–Newest Valid Study Plan