This page was exported from Testking Free Dumps [ http://blog.testkingfree.com ] Export date:Fri Feb 28 13:06:45 2025 / +0000 GMT ___________________________________________________ Title: [2025] Databricks-Certified-Data-Engineer-Associate.pdf - Questions Answers PDF Sample Questions Reliable [Q15-Q36] --------------------------------------------------- [2025] Databricks-Certified-Data-Engineer-Associate.pdf - Questions Answers PDF Sample Questions Reliable Databricks Databricks-Certified-Data-Engineer-Associate Dumps PDF Are going to be The Best Score The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is designed to validate the skills and knowledge of data engineers who work with the Databricks Unified Analytics Platform. Databricks Certified Data Engineer Associate Exam certification is ideal for professionals who want to demonstrate their expertise in building and optimizing data pipelines, data transformation, and data storage using Databricks. The Databricks Databricks-Certified-Data-Engineer-Associate exam is intended for data engineers, data architects, and developers who are responsible for designing, building, and maintaining data pipelines. Databricks Certified Data Engineer Associate Exam certification exam is comprised of 60 multiple-choice questions, and candidates have 90 minutes to complete the exam. Databricks-Certified-Data-Engineer-Associate exam measures candidates' knowledge and skills in various areas, including data ingestion, data transformation, and data processing.   NO.15 A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).Which of the following code blocks creates this SQL UDF?           Explanationhttps://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.htmlNO.16 A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.Which of the following approaches can the data engineer take to identify the table that is dropping the records?  They can set up separate expectations for each table when developing their DLT pipeline.  They cannot determine which table is dropping the records.  They can set up DLT to notify them via email when records are dropped.  They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.  They can navigate to the DLT pipeline page, click on the “Error” button, and review the present errors. NO.17 Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?  Cloud-specific integrations  Simplified governance  Ability to scale storage  Ability to scale workloads  Avoiding vendor lock-in One of the benefits of the Databricks Lakehouse Platform embracing open source technologies is that it avoids vendor lock-in. This means that customers can use the same open source tools and frameworks across different cloud providers, and migrate their data and workloads without being tied to a specific vendor. The Databricks Lakehouse Platform is built on open source projects such as Apache Spark, Delta Lake, MLflow, and Redash, which are widely used and trusted by millions of developers. By supporting these open source technologies, the Databricks Lakehouse Platform enables customers to leverage the innovation and community of the open source ecosystem, and avoid the risk of being locked into proprietary or closed solutions. The other options are either not related to open source technologies (A, B, C, D), or not benefits of the Databricks Lakehouse Platform (A, B). Reference: Databricks Documentation – Built on open source, Databricks Documentation – What is the Lakehouse Platform?, Databricks Blog – Introducing the Databricks Lakehouse Platform.NO.18 Which of the following is stored in the Databricks customer’s cloud account?  Databricks web application  Cluster management metadata  Repos  Data  Notebooks The only option that is stored in the Databricks customer’s cloud account is data. Data is stored in the customer’s cloud storage service, such as AWS S3 or Azure Data Lake Storage. The customer has full control and ownership of their data and can access it directly from their cloud account.Option A is not correct, as the Databricks web application is hosted and managed by Databricks on their own cloud infrastructure. The customer does not need to install or maintain the web application, but only needs to access it through a web browser.Option B is not correct, as the cluster management metadata is stored and managed by Databricks on their own cloud infrastructure. The cluster management metadata includes information such as cluster configuration, status, logs, and metrics. The customer can view and manage their clusters through the Databricks web application, but does not have direct access to the cluster management metadata.Option C is not correct, as the repos are stored and managed by Databricks on their own cloud infrastructure. Repos are version-controlled repositories that store code and data files for Databricks projects. The customer can create and manage their repos through the Databricks web application, but does not have direct access to the repos.Option E is not correct, as the notebooks are stored and managed by Databricks on their own cloud infrastructure. Notebooks are interactive documents that contain code, text, and visualizations for Databricks workflows. The customer can create and manage their notebooks through the Databricks web application, but does not have direct access to the notebooks.Reference:Databricks ArchitectureDatabricks Data SourcesDatabricks Repos[Databricks Notebooks][Databricks Data Engineer Professional Exam Guide]NO.19 Which of the following tools is used by Auto Loader process data incrementally?  Checkpointing  Spark Structured Streaming  Data Explorer  Unity Catalog  Databricks SQL NO.20 A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.Which of the following tools can the data engineer use to solve this problem?  Unity Catalog  Delta Lake  Databricks SQL  Data Explorer  Auto Loader Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage. Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. Reference: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live TablesNO.21 A data engineer has joined an existing project and they see the following query in the project repository:CREATE STREAMING LIVE TABLE loyal_customers ASSELECT customer_id –FROM STREAM(LIVE.customers)WHERE loyalty_level = ‘high’;Which of the following describes why the STREAM function is included in the query?  The STREAM function is not needed and will cause an error.  The table being created is a live table.  The customers table is a streaming live table.  The customers table is a reference to a Structured Streaming query on a PySpark DataFrame.  The data in the customers table has been updated since its last run. Explanationhttps://docs.databricks.com/en/sql/load-data-streaming-table.htmlLoad data into a streaming tableTo create a streaming table from data in cloud object storage, paste the following into the query editor, and then click Run:SQLCopy to clipboardCopy/* Load data from a volume */CREATE OR REFRESH STREAMING TABLE <table-name> ASSELECT * FROM STREAM read_files(‘/Volumes/<catalog>/<schema>/<volume>/<path>/<folder>’)/* Load data from an external location */CREATE OR REFRESH STREAMING TABLE <table-name> ASSELECT * FROM STREAM read_files(‘s3://<bucket>/<path>/<folder>’)NO.22 A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?  Databricks Repos automatically saves development progress  Databricks Repos supports the use of multiple branches  Databricks Repos allows users to revert to previous versions of a notebook  Databricks Repos provides the ability to comment on specific changes  Databricks Repos is wholly housed within the Databricks Lakehouse Platform Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. Reference: Git integration with Databricks Repos – Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available – New ‘Files’ Feature in …, Databricks Repos – What it is and how we can use it | Adatis.NO.23 In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?  Checkpointing and Write-ahead Logs  Structured Streaming cannot record the offset range of the data being processed in each trigger.  Replayable Sources and Idempotent Sinks  Write-ahead Logs and Idempotent Sinks  Checkpointing and Idempotent Sinks ExplanationThe engine uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. — in the link search for “The engine uses ” youll find the answer.https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#:~:text=The%20enginNO.24 A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.They have the following incomplete code block:____(f”SELECT customer_id, spend FROM {table_name}”)Which of the following can be used to fill in the blank to successfully complete the task?  spark.delta.sql  spark.delta.table  spark.table  dbutils.sql  spark.sql The spark.sql method can be used to execute SQL queries programmatically and return the result as a DataFrame. The spark.sql method accepts a string argument that contains a valid SQL statement. The data engineer can use a formatted string literal (f-string) to insert the Python variable table_name into the SQL query. The other methods are either invalid or not suitable for running SQL queries. References: Running SQL Queries Programmatically, Formatted string literals, spark.sqlNO.25 Which of the following benefits is provided by the array functions from Spark SQL?  An ability to work with data in a variety of types at once  An ability to work with data within certain partitions and windows  An ability to work with time-related data in specified intervals  An ability to work with complex, nested data ingested from JSON files  An ability to work with an array of tables for procedural automation ExplanationArray functions in Spark SQL are primarily used for working with arrays and complex, nested data structures, such as those often encountered when ingesting JSON files. These functions allow you to manipulate and query nested arrays and structures within your data, making it easier to extract and work with specific elements or values within complex data formats. While some of the other options (such as option A for working with different data types) are features of Spark SQL or SQL in general, array functions specifically excel at handling complex, nested data structures like those found in JSON files.NO.26 A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.Which of the following describes why Auto Loader inferred all of the columns to be of the string type?  All of the fields had at least one null value  There was a type mismatch between the specific schema and the inferred schema  JSON data is a text-based format  Auto Loader only works with string data  Auto Loader cannot infer the schema of ingested data NO.27 A data engineer only wants to execute the final block of a Python program if the Python variable day_of_week is equal to 1 and the Python variable review_period is True.Which of the following control flow statements should the data engineer use to begin this conditionally executed code block?  if day_of_week = 1 and review_period:  if day_of_week = 1 and review_period = “True”:  if day_of_week == 1 and review_period == “True”:  if day_of_week == 1 and review_period:  if day_of_week = 1 & review_period: = “True”: In Python, the == operator is used to compare the values of two variables, while the = operator is used to assign a value to a variable. Therefore, option A and E are incorrect, as they use the = operator for comparison.Option B and C are also incorrect, as they compare the review_period variable to a string value “True”, which is different from the boolean value True. Option D is the correct answer, as it uses the == operator to compare the day_of_week variable to the integer value 1, and the and operator to check if both conditions are true. If both conditions are true, then the final block of the Python program will be executed. References: [Python Operators], [Python If … Else]NO.28 A data engineer is working with two tables. Each of these tables is displayed below in its entirety.The data engineer runs the following query to join these tables together:Which of the following will be returned by the above query?  Option A  Option B  Option C  Option D  Option E Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns:customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.References: The use of INNER JOIN can be referenced from Databricks documentation on SQL JOIN or from other sources like W3Schools or GeeksforGeeks.NO.29 A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.Which of the following tools can the data engineer use to solve this problem?  Unity Catalog  Data Explorer  Databricks SQL  Delta Lake  Auto Loader Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage.Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. References: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live TablesNO.30 A data engineer has left the organization. The data team needs to transfer ownership of the data engineer’s Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?  Databricks account representative  This transfer is not possible  Workspace administrator  New lead data engineer  Original data engineer The workspace administrator is the only individual who can transfer ownership of the Delta tables in Data Explorer, assuming the original data engineer no longer has access. The workspace administrator has the highest level of permissions in the workspace and can manage all resources, users, and groups. The other options are either not possible or not sufficient to perform the ownership transfer. The Databricks account representative is not involved in the workspace management. The transfer is possible and not dependent on the original data engineer. The new lead data engineer may not have the necessary permissions to access or modify the Delta tables, unless granted by the workspace administrator or the original data engineer before leaving. Reference: Workspace access control, Manage Unity Catalog object ownership.NO.31 Which of the following commands will return the location of database customer360?  DESCRIBE LOCATION customer360;  DROP DATABASE customer360;  DESCRIBE DATABASE customer360;  ALTER DATABASE customer360 SET DBPROPERTIES (‘location’ = ‘/user’};  USE DATABASE customer360; NO.32 In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?  When the location of the data needs to be changed  When the target table is an external table  When the source table can be deleted  When the target table cannot contain duplicate records  When the source is not a Delta table ExplanationWith merge , you can avoid inserting the duplicate records. The dataset containing the new logs needs to be deduplicated within itself. By the SQL semantics of merge, it matches and deduplicates the new data with the existing data in the table, but if there is duplicate data within the new dataset, it is inserted.https://docs.databricks.com/en/delta/merge.html#:~:text=With%20merge%20%2C%20you%20can%20aNO.33 Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?  CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.  CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.  CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.  CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.  CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static. NO.34 A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.The code block used by the data engineer is below:If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?  processingTime(1)  trigger(availableNow=True)  trigger(parallelBatch=True)  trigger(processingTime=”once”)  trigger(continuous=”once”) Explanationhttps://stackoverflow.com/questions/71061809/trigger-availablenow-for-delta-source-streaming-queries-in-pyspaNO.35 A data engineer has joined an existing project and they see the following query in the project repository:CREATE STREAMING LIVE TABLE loyal_customers ASSELECT customer_id –FROM STREAM(LIVE.customers)WHERE loyalty_level = ‘high’;Which of the following describes why the STREAM function is included in the query?  The STREAM function is not needed and will cause an error.  The table being created is a live table.  The customers table is a streaming live table.  The customers table is a reference to a Structured Streaming query on a PySpark DataFrame.  The data in the customers table has been updated since its last run. The STREAM function is used to process data from a streaming live table or view, which is a table or view that contains data that has been added only since the last pipeline update. Streaming live tables and views are stateful, meaning that they retain the state of the previous pipeline run and only process new data based on the current query. This is useful for incremental processing of streaming or batch data sources. The customers table in the query is a streaming live table, which means that it contains the latest data from the source. The STREAM function enables the query to read the data from the customers table incrementally and create another streaming live table named loyal_customers, which contains the customer IDs of the customers with high loyalty level. Reference: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.NO.36 A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.Which of the following explains why the data files are no longer present?  The VACUUM command was run on the table  The TIME TRAVEL command was run on the table  The DELETE HISTORY command was run on the table  The OPTIMIZE command was nun on the table  The HISTORY command was run on the table ExplanationThe VACUUM command in Delta Lake is used to clean up and remove unnecessary data files that are no longer needed for time travel or query purposes. When you run VACUUMwith certain retention settings, it can delete older data files, which might include versions of data that are older than the specified retention period. If the data engineer is unable to restore the table to a version that is 3 days old because the data files have been deleted, it’s likely because the VACUUM command was run on the table, removing the older data files as part of data cleanup. Loading … Use Databricks-Certified-Data-Engineer-Associate Exam Dumps (2025 PDF Dumps) To Have Reliable Databricks-Certified-Data-Engineer-Associate Test Engine: https://www.testkingfree.com/Databricks/Databricks-Certified-Data-Engineer-Associate-practice-exam-dumps.html --------------------------------------------------- Images: https://blog.testkingfree.com/wp-content/plugins/watu/loading.gif https://blog.testkingfree.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2025-02-14 12:23:17 Post date GMT: 2025-02-14 12:23:17 Post modified date: 2025-02-14 12:23:17 Post modified date GMT: 2025-02-14 12:23:17