Which scenario is the LEAST suitable for using SparkML?

Training a random forest on 2 TB of data distributed across a cluster.

Performing cross-validation on a 100 GB dataset in a cluster.

Training a linear regression on a 50 MB CSV file on a single laptop.

Building a text classification pipeline with TF-IDF on millions of documents.

What is the primary purpose of using CrossValidator in SparkML?

To tune hyperparameters by evaluating the model on multiple folds.

To evaluate the model on a single holdout set.

To convert a DataFrame into an RDD for distributed processing.

To split the dataset into training and test sets.

A retail company has a 5 TB clickstream dataset stored in Delta Lake. They need to train a logistic regression model for real-time ad bidding, where the model must serve predictions with sub-10ms latency. The data science team is comfortable with Python and pandas. What should they do?

Train with SparkML and serve the model directly from a Spark cluster for each ad-bidding request.

Train with SparkML and use Structured Streaming to score each event as it arrives in the ad-bidding pipeline.

Train with SparkML on the full dataset, then export the model coefficients and implement the scoring logic in a single-node serving endpoint.

Use pandas to train a logistic regression model on a random 10% sample of the data, then serve it in a single-node endpoint.

DatabricksCertified Machine Learning Professional

Domain 1Objective 1

Using Spark ML MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 1)

Part of the Model Development domain, which accounts for 44% of the MACHINE-LEARNING-PROFESSIONAL exam. Databricks does not publish an official question count, but from its 120-minute exam (~50–80 total, ~22–35 in this domain), expect 6–9 from this objective — we provide 22 practice questions to prepare you well beyond it. (estimate)

22questions here

5free pages

7concepts

44%of the exam

Questions 1–5

1foundation · easy
Which scenario is the LEAST suitable for using SparkML?
Select an answer first
2application · medium
A data scientist is building a binary classification model to predict customer churn. The dataset has a mix of numeric and categorical features. They want to use a tree-based model that handles non-linear relationships and provides feature importance scores. Which SparkML estimator should they choose?
Select an answer first
3foundation · easy
Which SparkML estimator would you use to predict a continuous numeric value such as house price?
Select an answer first
4foundation · easy
What is the primary purpose of using CrossValidator in SparkML?
Select an answer first
5application · medium
A retail company has a 5 TB clickstream dataset stored in Delta Lake. They need to train a logistic regression model for real-time ad bidding, where the model must serve predictions with sub-10ms latency. The data science team is comfortable with Python and pandas. What should they do?
Select an answer first

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Using Spark ML MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 1)

Questions 1–5

Which scenario is the LEAST suitable for using SparkML?

Which SparkML estimator would you use to predict a continuous numeric value such as house price?

What is the primary purpose of using CrossValidator in SparkML?

A retail company has a 5 TB clickstream dataset stored in Delta Lake. They need to train a logistic regression model for real-time ad bidding, where the model must serve predictions with sub-10ms latency. The data science team is comfortable with Python and pandas. What should they do?

Keep studying — Model Development