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Databricks Databricks-Machine-Learning-Professional Exam Sample Questions

Question # 1

A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has alreadytuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files. Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?

A. Z-Ordering

B. Bin-packing

C. Write as a Parquet file

D. Data skipping

E. Tuning the file size

Answer

A. Z-Ordering

Explanation:

Z-Ordering is an optimization technique that can speed up the query by colocating similar records while considering values in multiple columns. Z-Ordering is a way of organizing data in storage based on the values of one or more columns. Z-Ordering maps multidimensional data to one dimension while preserving locality of the data points. This means that rows with similar values for the specified columns are stored close together in the same set of files. This improves the performance of queries that filter on those columns, as they can skip over irrelevant files or data blocks. Z-Ordering also enhances data skipping and caching, as it reduces the number of distinct values per file for the chosen columns1. The other options are incorrect because:

Option B: Bin-packing is an optimization technique that compacts small files into larger ones, but does not colocate similar records based on multiple columns. Bin-packing can improve the performance of queries by reducing the number of files that need to be read, but it does not affect the data layout within the files2.

Option C: Writing as a Parquet file is not an optimization technique, but a file format choice. Parquet is a columnar storage format that supports efficient compression and encoding schemes. Parquet can improve the performance of queries by reducing the storage footprint and the amount of data transferred, but it does not colocate similar records based on multiple columns3. Option D: Data skipping is an optimization technique that skips over files or data blocks that do not match the query predicates, but does not colocate similar records based on multiple columns. Data skipping can improve the performance of queries by avoiding unnecessary data scans, but it depends on the data layout and the metadata collected for each file4.

Option E: Tuning the file size is an optimization technique that adjusts the size of the data files to a target value, but does not colocate similar records based on multiple columns. Tuning the file size can improve the performance of queries by balancing the trade-off between parallelism and overhead, but it does not affectthe data layout within the files5.

References: Z-Ordering (multi-dimensional clustering), Compaction (bin-packing), Parquet, Data skipping, Tuning file sizes

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Question # 2

Which of the following describes concept drift?

A. Concept drift is when there is a change in the distribution of an input variable

B. Concept drift is when there is a change in the distribution of a target variable

C. Concept drift is when there is a change in the relationship between input variables and target variables

D. Concept drift is when there is a change in the distribution of the predicted target given by the model

E. None of these describe Concept drift

Answer

C. Concept drift is when there is a change in the relationship between input variables and target variables

Explanation:

Concept drift in machine learning and data mining refers to the change in the relationships between input and output data in the underlying problem over time. In other domains, this change maybe called “covariate shift,” “dataset shift,” or “nonstationarity.” Concept drift can affect the performance and accuracy of predictive models that assume a static relationship between input and output variables. Concept drift can be caused by various factors, such as changes in user behavior, environmental conditions, market trends, etc. Concept drift can be detected and handled by various methods, such as periodic retraining, online learning, ensemble methods, etc

References:

Concept drift - Wikipedia
A Gentle Introduction to Concept Drift in Machine Learning
Model Drift & Machine Learning: Concept Drift, Feature Drift, Etc.
Data Drift vs. Concept Drift: What Is the Difference?

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Question # 3

A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run. Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

A. mlflow.log_artifact

B. mlflow.log_model

C. mlflow.log_metric

D. mlflow.log_param

E. There is no way to store values like this.

Answer

D. mlflow.log_param

Explanation:

To log single values like the number of trees in a random forest, you can use the mlflow.log_param function. This function allows you to log a parameter (e.g. model hyperparameter) under the current run. Parameters can be of any type, and can be logged using the following syntax:

Python
mlflow.log_param("num_trees", 100)

AI-generated code. Review and use carefully. More info on FAQ.

MLflow also offers a convenient way to log multiple parameters by indicating all of them using a dictionary1. The other options are incorrect because:

Option A: mlflow.log_artifact is used to log output files in any format, not single values2.
Option B: mlflow.log_model is used to log an MLflow Model along with its artifacts, not single values3.
Option C: mlflow.log_metric is used to log numeric metrics, not single values4.
Option E: There is a way to store values like this using the mlflow.log_param function1.
References: mlflow.log_param, mlflow.log_artifact, mlflow.log_model, mlflow.log_metric

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Question # 4

After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set. Which of the following SQL commands can be used to accomplish this task?

A. VERSION

B. DESCRIBE

C. HISTORY

D. DESCRIBE HISTORY

Answer

D. DESCRIBE HISTORY

Explanation:

The DESCRIBE HISTORY command can be used to view the commit history of a Delta table, including the schema changes, operations, and timestamps. This command can help identify when a column was dropped from the feature set and by which operation. The other commands are either invalid or do not provide the required information. References:

Delta Lake - View Commit History
Databricks Certified Machine Learning Professional Exam Guide - Section 1: Experimentation - Data Management

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Question # 5

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.create_table

B. fs.write_table

C. fs.get_table

D. There is no way to accomplish this task with fs

E. fs.read_table

Answer

E. fs.read_table

Explanation:

The fs.read_table operation can be used to return a Spark DataFrame of a data set associated with a Feature Store table. This operation takes the name of the Feature Store table and an optional time travel specification as arguments. The fs.create_table operation is used to create a new Feature Store table from a Spark DataFrame. The fs.write_table operation is used to write data to an existing Feature Store table. The fs.get_table operation is used to get the metadata of a Feature Store table, not the data itself. There is a way to accomplish this task with fs, so option D is incorrect. References:

Feature Store Client

Feature Store Tables

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