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Databricks-Machine-Learning-Associate Actual Exam Dumps - Databricks-Machine-Learning-Associate New Study Guide

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

Topic
Details

Topic 1

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 2

Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.

Topic 3

ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.

Topic 4

Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
A data scientist has created a linear regression model that uses log(price) as a label variable. Using this model, they have performed inference and the predictions and actual label values are in Spark DataFrame preds_df.
They are using the following code block to evaluate the model:
regression_evaluator.setMetricName("rmse").evaluate(preds_df)
Which of the following changes should the data scientist make to evaluate the RMSE in a way that is comparable with price?

A. They should evaluate the MSE of the log predictions to compute the RMSE
B. They should exponentiate the predictions before computing the RMSE
C. They should take the log of the predictions before computing the RMSE
D. They should exponentiate the computed RMSE value

Answer: B

Explanation:
When evaluating the RMSE for a model that predicts log-transformed prices, the predictions need to be transformed back to the original scale to obtain an RMSE that is comparable with the actual price values. This is done by exponentiating the predictions before computing the RMSE. The RMSE should be computed on the same scale as the original data to provide a meaningful measure of error.
Reference:
Databricks documentation on regression evaluation: Regression Evaluation

NEW QUESTION # 18
A data scientist has been given an incomplete notebook from the data engineering team. The notebook uses a Spark DataFrame spark_df on which the data scientist needs to perform further feature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrame API.
Which of the following blocks of code can the data scientist run to be able to use the pandas API on Spark?

A. import pandas as pd
df = pd.DataFrame(spark_df)
B. import pyspark.pandas as ps
df = ps.to_pandas(spark_df)
C. import pyspark.pandas as ps
df = ps.DataFrame(spark_df)
D. spark_df.to_pandas()

Answer: C

Explanation:
To use the pandas API on Spark, the data scientist can run the following code block:
import pyspark.pandas as ps df = ps.DataFrame(spark_df)
This code imports the pandas API on Spark and converts the Spark DataFrame spark_df into a pandas-on-Spark DataFrame, allowing the data scientist to use familiar pandas functions for further feature engineering.
Reference:
Databricks documentation on pandas API on Spark: pandas API on Spark

NEW QUESTION # 19
A data scientist has defined a Pandas UDF function predict to parallelize the inference process for a single-node model:

They have written the following incomplete code block to use predict to score each record of Spark DataFrame spark_df:

Which of the following lines of code can be used to complete the code block to successfully complete the task?

A. predict(Iterator(spark_df))
B. mapInPandas(predict)
C. predict(*spark_df.columns)
D. mapInPandas(predict(spark_df.columns))
E. predict(spark_df.columns)

Answer: B

Explanation:
To apply the Pandas UDF predict to each record of a Spark DataFrame, you use the mapInPandas method. This method allows the Pandas UDF to operate on partitions of the DataFrame as pandas DataFrames, applying the specified function (predict in this case) to each partition. The correct code completion to execute this is simply mapInPandas(predict), which specifies the UDF to use without additional arguments or incorrect function calls.
Reference:
PySpark DataFrame documentation (Using mapInPandas with UDFs).

NEW QUESTION # 20
A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:

A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model.
Which of the following is a negative consequence of the approach suggested by the colleague?

A. The feature engineering stages will be computed using validation data
B. The model will take longer to train for each unique combination of hvperparameter values
C. The cross-validation process will no longer be
D. The model will be refit one more per cross-validation fold
E. The cross-validation process will no longer be reproducible

Answer: A

Explanation:
If the model object is passed to the estimator parameter of CrossValidator and the cross-validation object itself is placed as a stage in the pipeline, the feature engineering stages within the pipeline would be applied separately to each training and validation fold during cross-validation. This leads to a significant issue: the feature engineering stages would be computed using validation data, thereby leaking information from the validation set into the training process. This would potentially invalidate the cross-validation results by giving an overly optimistic performance estimate.
Reference:
Cross-validation and Pipeline Integration in MLlib (Avoiding Data Leakage in Pipelines).

NEW QUESTION # 21
Which of the following approaches can be used to view the notebook that was run to create an MLflow run?

A. Open the MLmodel artifact in the MLflow run paqe
B. Click the "Models" link in the row corresponding to the run in the MLflow experiment paqe
C. Click the "Source" link in the row corresponding to the run in the MLflow experiment page
D. Click the "Start Time" link in the row corresponding to the run in the MLflow experiment page

Answer: C

Explanation:
To view the notebook that was run to create an MLflow run, you can click the "Source" link in the row corresponding to the run in the MLflow experiment page. The "Source" link provides a direct reference to the source notebook or script that initiated the run, allowing you to review the code and methodology used in the experiment. This feature is particularly useful for reproducibility and for understanding the context of the experiment.
Reference:
MLflow Documentation (Viewing Run Sources and Notebooks).

NEW QUESTION # 22
......

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