This page was exported from Valid Premium Exam [ http://premium.validexam.com ] Export date:Sun Feb 23 12:40:11 2025 / +0000 GMT ___________________________________________________ Title: [Feb-2025] 100% Actual Databricks-Certified-Data-Engineer-Associate dumps Q&As with Explanations Verified & Correct Answers [Q31-Q51] --------------------------------------------------- [Feb-2025] 100% Actual Databricks-Certified-Data-Engineer-Associate dumps Q&As with Explanations Verified & Correct Answers Databricks-Certified-Data-Engineer-Associate Dumps with Free 365 Days Update Fast Exam Updates NO.31 A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team’s queries uses the same SQL endpoint.Which of the following approaches can the data engineering team use to improve the latency of the team’s queries?  They can increase the cluster size of the SQL endpoint.  They can increase the maximum bound of the SQL endpoint’s scaling range.  They can turn on the Auto Stop feature for the SQL endpoint.  They can turn on the Serverless feature for the SQL endpoint.  They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to “Reliability Optimized.” https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions-from-query/td-p/36696NO.32 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”: ExplanationThis statement will check if the variable day_of_week is equal to 1 and if the variable review_period evaluates to a truthy value. The use of the double equal sign (==) in the comparison of day_of_week is important, as a single equal sign (=) would be used to assign a value to the variable instead of checking its value. The use of a single ampersand (&) instead of the keyword and is not valid syntax in Python. The use of quotes around True in options B and C will result in a string comparison, which will not evaluate to True even if the value of review_period is True.NO.33 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 cade block used by the data engineer is below:If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?  trigger(“5 seconds”)  trigger()  trigger(once=”5 seconds”)  trigger(processingTime=”5 seconds”)  trigger(continuous=”5 seconds”) NO.34 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 Structured Streaming uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. This ensures that the engine can reliably track the exact progress of the processing and handle any kind of failure by restarting and/or reprocessing. Checkpointing is the mechanism of saving the state of a streaming query to fault-tolerant storage (such as HDFS) so that it can be recovered after a failure.Write-ahead logs are files that record the offset range of the data being processed in each trigger and are written to the checkpoint location before the processing starts. These logs are used to recover the query state and resume processing from the last processed offset range in case of a failure. References: Structured Streaming Programming Guide, Fault Tolerance SemanticsNO.35 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 NO.36 Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?           The best practice is to use “Complete” as output mode instead of “append” when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is optionNO.37 A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.Which of the following commands can be used to grant the necessary permission on the entire database to the new team?  GRANT VIEW ON CATALOG customers TO team;  GRANT CREATE ON DATABASE customers TO team;  GRANT USAGE ON CATALOG team TO customers;  GRANT CREATE ON DATABASE team TO customers;  GRANT USAGE ON DATABASE customers TO team; The correct command to grant the necessary permission on the entire database to the new team is to use the GRANT USAGE command. The GRANT USAGE command grants the principal the ability to access the securable object, such as a database, schema, or table. In this case, the securable object is the database customers, and the principal is the group team. By granting usage on the database, the team will be able to see what tables already exist in the database. Option E is the only option that uses the correct syntax and the correct privilege type for this scenario. Option A uses the wrong privilege type (VIEW) and the wrong securable object (CATALOG). Option B uses the wrong privilege type (CREATE), which would allow the team to create new tables in the database, but not necessarily see the existing ones. Option C uses the wrong securable object (CATALOG) and the wrong principal (customers). Option D uses the wrong securable object (team) and the wrong principal (customers). References: GRANT, Privilege types, Securable objects, PrincipalsNO.38 A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:DROP TABLE IF EXISTS my_table;After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.Which of the following describes why all of these files were deleted?  The table was managed  The table’s data was smaller than 10 GB  The table’s data was larger than 10 GB  The table was external  The table did not have a location The reason why all of the data files and metadata files were deleted from the file system after dropping the table is that the table was managed. A managed table is a table that is created and managed by Spark SQL. It stores both the data and the metadata in the default location specified by the spark.sql.warehouse.dir configuration property. When a managed table is dropped, both the data and the metadata are deleted from the file system.Option B is not correct, as the size of the table’s data does not affect the behavior of dropping the table.Whether the table’s data is smaller or larger than 10 GB, the data files and metadata files will be deleted if the table is managed, and will be preserved if the table is external.Option C is not correct, for the same reason as option B.Option D is not correct, as an external table is a table that is created and managed by the user. It stores the data in a user-specified location, and only stores the metadata in the Spark SQL catalog. When an external table is dropped, only the metadata is deleted from the catalog, but the data files are preserved in the file system.Option E is not correct, as a table must have a location to store the data. If the location is not specified by the user, it will use the default location for managed tables. Therefore, a table without a location is a managed table, and dropping it will delete both the data and the metadata.References:* Managing Tables* [Databricks Data Engineer Professional Exam Guide]NO.39 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 NO.40 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 The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. References: 1: Spark SQL, Built-in Functions – Apache Spark 2: Spark SQL Array Functions Complete List – Spark By Examples 3: Spark SQL Array Functions – Syntax and Examples – DWgeek.com 4: Spark SQL, Built-in Functions – Apache Spark : Spark SQL, Built-in Functions – Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks – The Databricks Blog]NO.41 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.42 A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.Which of the following commands could the data engineering team use to access sales in PySpark?  SELECT * FROM sales  There is no way to share data between PySpark and SQL.  spark.sql(“sales”)  spark.delta.table(“sales”)  spark.table(“sales”) The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.forPath method to load the Delta table from its path2. References: 1: SparkSession | PySpark3.2.0 documentation 2: Welcome to Delta Lake’s Python documentation page – delta-spark 2.4.0 documentationNO.43 A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when It is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?  O They can reduce the cluster size of the SQL endpoint.  Q They can turn on the Auto Stop feature for the SQL endpoint.  O They can set up the dashboard’s SQL endpoint to be serverless.  0 They can ensure the dashboard’s SQL endpoint matches each of the queries’ SQL endpoints. To minimize the total running time of the SQL endpoint used in the refresh schedule of a dashboard in Databricks, the most effective approach is to utilize the Auto Stop feature. This feature allows the SQL endpoint to automatically stop after a period of inactivity, ensuring that it only runs when necessary, such as during the dashboard refresh or when actively queried. This minimizes resource usage and associated costs by ensuring the SQL endpoint is not running idle outside of these operations.Reference:Databricks documentation on SQL endpoints: SQL Endpoints in DatabricksNO.44 Which of the following describes the storage organization of a Delta table?  Delta tables are stored in a single file that contains data, history, metadata, and other attributes.  Delta tables store their data in a single file and all metadata in a collection of files in a separate location.  Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.  Delta tables are stored in a collection of files that contain only the data stored within the table.  Delta tables are stored in a single file that contains only the data stored within the table. ExplanationDelta tables store data in a structured manner using Parquet files, and they also maintain metadata and transaction logs in separate directories. This organization allows for versioning, transactional capabilities, and metadata tracking in Delta Lake. Thank you for pointing out the error, and I appreciate your understanding.NO.45 A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.Which of the following commands can be used to grant the necessary permission on the entire database to the new team?  GRANT VIEW ON CATALOG customers TO team;  GRANT CREATE ON DATABASE customers TO team;  GRANT USAGE ON CATALOG team TO customers;  GRANT CREATE ON DATABASE team TO customers;  GRANT USAGE ON DATABASE customers TO team; ExplanationThe GRANT statement is used to grant privileges on a database, table, or view to a user or role. The ALL PRIVILEGES option grants all possible privileges on the specified object, such as CREATE, SELECT, MODIFY, and USAGE. The syntax of the GRANT statement is:GRANT privilege_type ON object TO user_or_role;Therefore, to grant full permissions on the database customers to the new data engineering team, the command should be:GRANT ALL PRIVILEGES ON DATABASE customers TO team;NO.46 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. A streaming live table or view processes data that has been added only since the last pipeline update.Streaming tables and views are stateful; if the defining query changes, new data will be processed based on the new query and existing data is not recomputed. This is useful when data needs to be processed incrementally, such as when ingesting streaming data sources or performing incremental loads from batch data sources. A live table or view, on the other hand, may be entirely computed when possible to optimize computation resources and time. This is suitable when data needs to be processed in full, such as when performing complex transformations or aggregations that require scanning all the data. References: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.NO.47 Which of the following describes the type of workloads that are always compatible with Auto Loader?  Dashboard workloads  Streaming workloads  Machine learning workloads  Serverless workloads  Batch workloads Auto Loader is a Structured Streaming source that incrementally and efficiently processes new data files as they arrive in cloud storage. It supports both Python and SQL in Delta Live Tables, which are ideal for building streaming data pipelines. Auto Loader can handle near real-time ingestion of millions of files per hour and provide exactly-once guarantees when writing data into Delta Lake. Auto Loader is not designed for dashboard, machine learning, serverless, or batch workloads, which have different requirements and characteristics. Reference: What is Auto Loader?, Delta Live TablesNO.48 A data engineer has a Python notebook in Databricks, but they need to use SQL to accomplish a specific task within a cell. They still want all of the other cells to use Python without making any changes to those cells.Which of the following describes how the data engineer can use SQL within a cell of their Python notebook?  It is not possible to use SQL in a Python notebook  They can attach the cell to a SQL endpoint rather than a Databricks cluster  They can simply write SQL syntax in the cell  They can add %sql to the first line of the cell  They can change the default language of the notebook to SQL In Databricks, you can use different languages within the same notebook by using magic commands. Magic commands are special commands that start with a percentage sign (%) and allow you to change the behavior of the cell. To use SQL within a cell of a Python notebook, you can add %sql to the first line of the cell. This will tell Databricks to interpret the rest of the cell as SQL code and execute it against the default database. You can also specify a different database by using the USE statement. The result of the SQL query will be displayed as a table or a chart, depending on the output mode. You can also assign the result to a Python variable by using the -o option. For example, %sql -o df SELECT * FROM my_table will run the SQL query and store the result as a pandas DataFrame in the Python variable df. Option A is incorrect, as it is possible to use SQL in a Python notebook using magic commands. Option B is incorrect, as attaching the cell to a SQL endpoint is not necessary and will not change the language of the cell. Option C is incorrect, as simply writing SQL syntax in the cell will result in a syntax error, as the cell will still be interpreted as Python code. Option E is incorrect, as changing the default language of the notebook to SQL will affect all the cells, not just one. Reference: Use SQL in Notebooks – Knowledge Base – Noteable, [SQL magic commands – Databricks], [Databricks SQL Guide – Databricks]NO.49 A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?  None of these changes will need to be made  The pipeline will need to stop using the medallion-based multi-hop architecture  The pipeline will need to be written entirely in SQL  The pipeline will need to use a batch source in place of a streaming source  The pipeline will need to be written entirely in Python NO.50 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 ExplanationAn advantage of using Databricks Repos over the built-in Databricks Notebooks versioning is the ability to work with multiple branches. Branching is a fundamental feature ofversion control systems like Git, which Databricks Repos is built upon. It allows you to create separate branches for different tasks, features, or experiments within your project. This separation helps in parallel development and experimentation without affecting the main branch or the work of other team members. Branching provides a more organized and collaborative development environment, making it easier to merge changes and manage different development efforts. While Databricks Notebooks versioning also allows you to track versions of notebooks, it may not provide the same level of flexibility and collaboration as branching in Databricks Repos.NO.51 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”) https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter Loading … Verified Databricks-Certified-Data-Engineer-Associate dumps Q&As - 2025 Latest Databricks-Certified-Data-Engineer-Associate Download: https://www.validexam.com/Databricks-Certified-Data-Engineer-Associate-latest-dumps.html --------------------------------------------------- Images: https://premium.validexam.com/wp-content/plugins/watu/loading.gif https://premium.validexam.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2025-02-21 13:24:43 Post date GMT: 2025-02-21 13:24:43 Post modified date: 2025-02-21 13:24:43 Post modified date GMT: 2025-02-21 13:24:43