Sharpen Your Knowledge with Databricks (Databricks Certified Machine Learning Associate Exam) Certification Sample Questions
CertsTime has provided you with a sample question set to elevate your knowledge about the Databricks Certified Machine Learning Associate Exam . With these updated sample questions, you can become quite familiar with the difficulty level and format of the real Databricks Certified Machine Learning Associate Exam certification test. Try our sample Databricks Certified Machine Learning Associate Exam certification practice exam to get a feel for the real exam environment. Our sample practice exam gives you a sense of reality and an idea of the questions on the actual Databricks Machine Learning Associate certification exam.
Our sample questions are similar to the Real Databricks Certified Machine Learning Associate Exam questions. The premium Databricks Certified Machine Learning Associate Exam certification practice exam gives you a golden opportunity to evaluate and strengthen your preparation with real-time scenario-based questions. Plus, by practicing real-time scenario-based questions, you will run into a variety of challenges that will push you to enhance your knowledge and skills.
Databricks Certified Machine Learning Associate Exam Sample Questions:
A data scientist wants to use Spark ML to one-hot encode the categorical features in their PySpark DataFrame features_df. A list of the names of the string columns is assigned to the input_columns variable.
They have developed this code block to accomplish this task:
The code block is returning an error.
Which of the following adjustments does the data scientist need to make to accomplish this task?
A data scientist wants to use Spark ML to impute missing values in their PySpark DataFrame features_df. They want to replace missing values in all numeric columns in features_df with each respective numeric column's median value.
They have developed the following code block to accomplish this task:
The code block is not accomplishing the task.
Which reasons describes why the code block is not accomplishing the imputation task?
Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?
A machine learning engineer is trying to perform batch model inference. They want to get predictions using the linear regression model saved at the path model_uri for the DataFrame batch_df.
batch_df has the following schema:
customer_id STRING
The machine learning engineer runs the following code block to perform inference on batch_df using the linear regression model at model_uri:
In which situation will the machine learning engineer's code block perform the desired inference?
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?
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