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Databricks Certified Machine Learning Associate Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
A data scientist has written a data cleaning notebook that utilizes the pandas library, but their colleague has suggested that they refactor their notebook to scale with big data.
Which of the following approaches can the data scientist take to spend the least amount of time refactoring their notebook to scale with big data?

A. They can refactor their notebook to utilize the pandas API on Spark.
B. They can refactor their notebook to use Spark SQL.
C. They can refactor their notebook to process the data in parallel.
D. They can refactor their notebook to use the PySpark DataFrame API.
E. They can refactor their notebook to use the Scala Dataset API.

Answer: A

Explanation:
The data scientist can refactor their notebook to utilize the pandas API on Spark (now known as pandas on Spark, formerly Koalas). This allows for the least amount of changes to the existing pandas-based code while scaling to handle big data using Spark's distributed computing capabilities. pandas on Spark provides a similar API to pandas, making the transition smoother and faster compared to completely rewriting the code to use PySpark DataFrame API, Scala Dataset API, or Spark SQL.
Reference:
Databricks documentation on pandas API on Spark (formerly Koalas).

NEW QUESTION # 54
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. None of these compute tools support this task
C. SQL Warehouse
D. Standard cluster

Answer: D

Explanation:
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.
Reference:
Databricks documentation on clusters: Clusters in Databricks

NEW QUESTION # 55
A data scientist learned during their training to always use 5-fold cross-validation in their model development workflow. A colleague suggests that there are cases where a train-validation split could be preferred over k-fold cross-validation when k > 2.
Which of the following describes a potential benefit of using a train-validation split over k-fold cross-validation in this scenario?

A. Fewer models need to be trained when using a train-validation split
B. Bias is avoidable when using a train-validation split
C. Reproducibility is achievable when using a train-validation split
D. A holdout set is not necessary when using a train-validation split
E. Fewer hyperparameter values need to be tested when using a train-validation split

Answer: A

Explanation:
A train-validation split is often preferred over k-fold cross-validation (with k > 2) when computational efficiency is a concern. With a train-validation split, only two models (one on the training set and one on the validation set) are trained, whereas k-fold cross-validation requires training k models (one for each fold).
This reduction in the number of models trained can save significant computational resources and time, especially when dealing with large datasets or complex models.
Reference:
Model Evaluation with Train-Test Split

NEW QUESTION # 56
Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

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

Answer: E

Explanation:
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 more efficient than processing data row by row as is typical with standard PySpark UDFs, which can significantly speed up the computation.
Reference
PySpark Documentation on UDFs: https://spark.apache.org/docs/latest/api/python/user_guide/sql/arrow_pandas.html#pandas-udfs-a-k-a-vectorized-udfs

NEW QUESTION # 57
A data scientist is developing a machine learning pipeline using AutoML on Databricks Machine Learning.
Which of the following steps will the data scientist need to perform outside of their AutoML experiment?

A. Exploratory data analysis
B. Model tuning
C. Model evaluation
D. Model deployment

Answer: A

Explanation:
AutoML platforms, such as the one available in Databricks Machine Learning, streamline various stages of the machine learning pipeline including feature engineering, model selection, hyperparameter tuning, and model evaluation. However, exploratory data analysis (EDA) is typically performed outside the AutoML process. EDA involves understanding the dataset, visualizing distributions, identifying anomalies, and gaining insights into data before feeding it into a machine learning pipeline. This step is crucial for ensuring that the data is clean and suitable for model training but is generally done manually by the data scientist.
Reference
Databricks documentation on AutoML: https://docs.databricks.com/applications/machine-learning/automl.html

NEW QUESTION # 58
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