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

Topic	Details
Topic 1	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 4	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
Topic 5	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift
Topic 6	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 7	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 8	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook

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Databricks Certified Machine Learning Professional Sample Questions (Q58-Q63):

NEW QUESTION # 58
A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

A. Replace "inference" with the path to the location of the Delta table
B. Replace formatfdelta") with format("stream")
C. Replace spark.read with spark.readStream
D. Replace predict with a stream-friendly prediction function
E. Replace schema(schema) with option("maxFilesPerTriqqer", 1}

Answer: E

NEW QUESTION # 59
Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?

A. fs.write_table
B. fs.create_table
C. There is no way to accomplish this task with fs
D. fs.read_table
E. fs.get_table

Answer: B

NEW QUESTION # 60
A Data Scientist is building a machine learning pipeline to classify raw text using a Logistic Regression model in Spark using Spark MLlib's Pipelines. This pipeline has three stages: the Tokenizer (to split the raw text in tokens), a HashingTF (to transform tokens into hashes) and the Logistic Regression itself (to perform the classification of texts). The Spark DataFrame with the training data is called trainingDF and the one with the test data is called testDF.
In order to do this, they use the following incomplete piece of code:

Which option correctly states:
(i) The complete command to run model training;
(ii) The complete command to execute the prediction on test data;
(iii) The object type of the model object returned by the model
training command.

Answer: B

Explanation:
In Spark MLlib, a Pipeline is trained using the fit method, which applies all estimator stages to the training DataFrame and returns a PipelineModel. Predictions are generated by calling transform on the fitted PipelineModel, which applies the full pipeline (tokenization, feature hashing, and logistic regression) to the test DataFrame.

NEW QUESTION # 61
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

A. Remove the nested=True argument from the child runs
B. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
C. Add the nested=True argument to the parent run
D. Indent the child run blocks within the parent run block
E. Provide the same name to the run name parameter for all three run blocks

Answer: B

NEW QUESTION # 62
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

A. features_df.write.mode("fs").path("new_table")
B.
C. features_df.write.mode("feature").path("new_table")
D.
E.

Answer: B

NEW QUESTION # 63
......

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