FreeQAs
 Request Exam  Contact
  • Home
  • View All Exams
  • New QA's
  • Upload
PRACTICE EXAMS:
  • Oracle
  • Fortinet
  • Juniper
  • Microsoft
  • Cisco
  • Citrix
  • CompTIA
  • VMware
  • ISC
  • SAP
  • EMC
  • PMI
  • HP
  • Salesforce
  • Other
  • Oracle
    Oracle
  • Fortinet
    Fortinet
  • Juniper
    Juniper
  • Microsoft
    Microsoft
  • Cisco
    Cisco
  • Citrix
    Citrix
  • CompTIA
    CompTIA
  • VMware
    VMware
  • ISC
    ISC
  • SAP
    SAP
  • EMC
    EMC
  • PMI
    PMI
  • HP
    HP
  • Salesforce
    Salesforce
  1. Home
  2. Databricks Certification
  3. Databricks-Machine-Learning-Associate Exam
  4. Databricks.Databricks-Machine-Learning-Associate.v2025-05-09.q25 Dumps
  • «
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • »
Download Now

Question 11

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

Correct Answer: C
Pandas API on Spark (previously known as Koalas) provides a pandas-like API on top of Apache Spark. It allows users to perform pandas operations on large datasets using Spark's distributed compute capabilities. Internally, it uses Spark DataFrames and adds metadata that facilitates handling operations in a pandas-like manner, ensuring compatibility and leveraging Spark's performance and scalability.
Reference
pandas API on Spark documentation: https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html
insert code

Question 12

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?

Correct Answer: B
The code block to compute the root mean-squared error (RMSE) for a linear regression model in Spark ML should use the RegressionEvaluator class with metricName set to "rmse". Given the schema of preds_df with columns prediction and actual, the correct evaluator setup will specify predictionCol="prediction" and labelCol="actual". Thus, the appropriate code block (Option C in your list) that uses RegressionEvaluator to compute the RMSE is the correct choice. This setup correctly measures the performance of the regression model using the predictions and actual outcomes from the DataFrame.
Reference:
Spark ML documentation (Using RegressionEvaluator to Compute RMSE).
insert code

Question 13

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

Correct Answer: C
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).
insert code

Question 14

A data scientist has developed a random forest regressor rfr and included it as the final stage in a Spark MLPipeline pipeline. They then set up a cross-validation process with pipeline as the estimator in the following code block:

Which of the following is a negative consequence of including pipeline as the estimator in the cross-validation process rather than rfr as the estimator?

Correct Answer: A
Including the entire pipeline as the estimator in the cross-validation process means that all stages of the pipeline, including data preprocessing steps like string indexing and vector assembling, will be refit or retransformed for each fold of the cross-validation. This results in a longer runtime because each fold requires re-execution of these preprocessing steps, which can be computationally expensive.
If only the random forest regressor (rfr) were included as the estimator, the preprocessing steps would be performed once, and only the model fitting would be repeated for each fold, significantly reducing the computational overhead.
Reference:
Databricks documentation on cross-validation: Cross Validation
insert code

Question 15

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?

Correct Answer: D
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
insert code
  • «
  • 1
  • 2
  • 3
  • 4
  • 5
  • 6
  • »
[×]

Download PDF File

Enter your email address to download Databricks.Databricks-Machine-Learning-Associate.v2025-05-09.q25 Dumps

Email:

FreeQAs

Our website provides the Largest and the most Latest vendors Certification Exam materials around the world.

Using dumps we provide to Pass the Exam, we has the Valid Dumps with passing guranteed just which you need.

  • DMCA
  • About
  • Contact Us
  • Privacy Policy
  • Terms & Conditions
©2026 FreeQAs

www.freeqas.com materials do not contain actual questions and answers from Cisco's certification exams.