Question # 1
| 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 |
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
Question # 2
| 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 |
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
Question # 3
| A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks.
Which of the following locations in Databricks will show these data visualizations? | | A. The MLflow Model RegistryModel paqe | | B. The Artifacts section of the MLflow Experiment page | | C. Logged data visualizations cannot be viewed in Databricks | | D. The Artifacts section of the MLflow Run page | | E. The Figures section of the MLflow Run page |
D. The Artifacts section of the MLflow Run page
Explanation:
To view the data visualizations that are logged with each run, you can go to the Artifacts section of the MLflow Run page in Databricks. The Artifacts section shows the files and directories that are logged as artifacts for a run. You can browse the artifact hierarchy and preview the files, such as images, text, or HTML1. You can also download the artifacts or copy their URIs for further use2. The other options are incorrect because:
Option A: The MLflow Model Registry Model page shows the information and metadata of a registered model, such as its name, description, versions, stages, and lineage. It does not show the data visualizations that are logged with each run3.
Option B: The Artifacts section of the MLflow Experiment page shows the artifacts that are logged for an experiment, not for a specific run. It does not allow you to preview the files or browse the artifact hierarchy4.
Option C: Logged data visualizations can be viewed in Databricks using the Artifacts section of the MLflow Run page1.
Option E: There is no Figures section of the MLflow Run page in Databricks. The Figures section is only available in the open source MLflow UI, which shows the plots that are logged as figures for a run5. References: View run artifacts, Log, list, and download artifacts, Manage models, View experiment artifacts, Logging Visualizations with MLflow
Question # 4
| 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 |
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
Question # 5
| A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches.
Which of the following tools can be used to provide this type of continuous processing? | | A. Spark UDFs | | B. [Structured Streaming | | C. MLflow
D Delta Lake | | D. AutoML |
B. [Structured Streaming
Explanation:
Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. It allows users to express streaming computations the same way as batch computations on static data, using DataFrame and Dataset APIs. Structured Streaming can handle both unbounded and bounded data sources, and can process data in micro-batches or continuously. Structured Streaming can be used to provide continuous processing of data for machine learning applications, such as data ingestion, preprocessing, feature engineering, and model inference. StructuredStreaming can also integrate with MLflow and Delta Lake to enable end-to-end machine learning pipelines with tracking, reproducibility, and governance123
References:
Structured Streaming Programming Guide - Spark 3.2.0 Documentation
Structured Streaming + Kafka Integration Guide (Kafka broker version 0.10.0 or higher) - Spark 3.2.0 Documentation
Machine Learning with Structured Streaming - Databricks
[Continuous Machine Learning with Structured Streaming and MLflow - Databricks]
Question # 6
| A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in themodel_urivariable and its Run ID in therun_idvariable. They have also determined that the model was logged with the name"model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name"best_model".
Which of the following lines of code can they use to register the model to the MLflow Model Registry? | | A. mlflow.register_model(model_uri, "best_model") | | B. mlflow.register_model(run_id, "best_model") | | C. mlflow.register_model(f"runs:/{run_id}/best_model", "model") | | D. mlflow.register_model(model_uri, "model") | | E. mlflow.register_model(f"runs:/{run_id}/model") |
A. mlflow.register_model(model_uri, "best_model")
Explanation:
The mlflow.register_model function takes two arguments: model_uri and name. The model_uri is the URI of the model that was logged to MLflow, which can be obtained from the best run object. The name is the name of the registered model in the MLflow Model Registry. Therefore, the correct line of code to register the model is:
mlflow.register_model(model_uri, “best_model”)
This will create a new registered model with the name “best_model” and register the model version from the best run as the first version of that model.
References:
[mlflow.register_model — MLflow 1.22.0 documentation]
[MLflow Model Registry — Databricks Documentation]
[Manage MLflow Models — Databricks Documentation] Message has links.
Question # 7
| Which of the following is a simple statistic to monitor for categorical feature drift? | | A. Mode | | B. None of these | | C. Mode, number of unique values, and percentage of missing values | | D. Percentage of missing values | | E. Number of unique values |
C. Mode, number of unique values, and percentage of missing values
Explanation:
Categorical feature drift is a change in the distribution of the input data over time for categorical features, which can affect the performance and accuracy of the model. Monitoring categorical feature drift is important to ensure that the model is still valid and reliable for the current data. One simple statistic to monitor for categorical feature drift is the combination of mode, number of unique values, and percentage of missing values for each categorical feature. These statistics can provide a quick overview of the changes in the data distribution, such as the most frequent category, the diversity of categories, and the quality of data. If these statistics deviate significantly from the baseline values, it may indicate a categorical feature drift. However, these statistics may not capture all the nuances of the data distribution, such as the relative frequencies of different categories, the similarity of categories, etc. Therefore, other methods, such as statistical tests or information-theoretic measures, may be needed to complement the simple statistics and provide a more comprehensive analysis of the categorical feature drift123 References:
Monitoring Feature Drift - Databricks
Drift Metrics: How to Select the Right Metric to Analyze Drift
Detect data drift on datasets (preview) - Azure Machine Learning
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The exam emphasizes three core competency areas (with their
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detect data drift, a phenomenon where the underlying data distribution
changes over time, potentially impacting model performance.
Benefits of Certification
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FAQs of Databricks-Machine-Learning-Professional Exam
What is the Databricks Certified Machine Learning Professional certification exam?
The Databricks Certified Machine Learning Professional certification
exam evaluates individuals proficiency in using Databricks Machine
Learning to conduct advanced machine learning tasks in production
settings.
What does the Databricks Certified Machine Learning Professional certification exam assess?
The exam assesses an individual's ability to utilize Databricks
Machine Learning for advanced machine learning tasks, including
experimentation, model lifecycle management, model deployment, and
solution and data monitoring.
What are the main areas covered in the certification exam?
The exam covers experimentation (30%), Model lifecycle management
(30%), Model deployment (25%), solution and data monitoring (15%).
What is the format of the certification exam?
The exam consists of 60 multiple-choice questions and has a time limit of 120 minutes.
How much is the registration fee for the Databricks Certified Machine Learning Professional exam?
The Databricks Certified Machine Learning Professional Exam registration fee is $200.
What languages are available for the certification exam?
The exam is available only in English.
Is any prior training required before taking the certification exam?
No prerequisites are required, but related training is highly recommended.
What is the recommended experience level for candidates taking the certification exam?
Candidates should have at least 1+ years of hands-on experience
performing the machine learning tasks outlined in the exam guide.
How long is the validity period of the certification?
The certification is valid for two years.
What is the recertification process for maintaining the certified status?
Recertification is required every two years, and candidates must
take the current version of the exam to maintain certified status.
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