This page was exported from Valid Premium Exam [ http://premium.validexam.com ] Export date:Thu Nov 14 16:38:00 2024 / +0000 GMT ___________________________________________________ Title: [Sep 28, 2024] Genuine Databricks-Certified-Data-Engineer-Associate Exam Dumps New 2024 Databricks Pratice Exam [Q20-Q40] --------------------------------------------------- [Sep 28, 2024] Genuine Databricks-Certified-Data-Engineer-Associate Exam Dumps New 2024 Databricks Pratice Exam New 2024 Realistic Databricks-Certified-Data-Engineer-Associate Dumps Test Engine Exam Questions in here The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a comprehensive and rigorous examination that validates a candidate's understanding of the Databricks platform and its role in data engineering. Databricks Certified Data Engineer Associate Exam certification focuses on assessing the practical skills and knowledge required to design, build, and maintain data pipelines using Databricks. The Databricks Databricks-Certified-Data-Engineer-Associate exam covers a range of topics, including data ingestion, processing, and storage. Candidates are tested on their ability to design and implement data pipelines, data models, and data processing workflows using Databricks. Databricks is a cloud-based data platform that provides a unified analytics platform for data engineering, data science, and machine learning.   NEW QUESTION 20A 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;NEW QUESTION 21A 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 The VACUUM command is used to remove files that are no longer referenced by a Delta table and are older than the retention threshold1. The default retention period is 7 days2, but it can be changed by setting the delta.logRetentionDuration and delta.deletedFileRetentionDuration configurations3. If the VACUUM command was run on the table with a retention period shorter than 3 days, then the data files that were needed to restore the table to a 3-day-old version would have been deleted. The other commands do not delete data files from the table. The TIME TRAVEL command is used to query a historical version of the table4. The DELETE HISTORY command is not a valid command in Delta Lake. The OPTIMIZE command is used to improve the performance of the table by compacting small files into larger ones5. The HISTORY command is used to retrieve information about the operations performed on the table. References: 1: VACUUM | Databricks on AWS 2: Work with Delta Lake table history | Databricks on AWS 3: [Delta Lake configuration | Databricks on AWS] 4: Work with Delta Lake table history – Azure Databricks 5: [OPTIMIZE | Databricks on AWS] : [HISTORY | Databricks on AWS]NEW QUESTION 22Which of the following SQL keywords can be used to convert a table from a long format to a wide format?  PIVOT  CONVERT  WHERE  TRANSFORM  SUM ExplanationThe SQL keyword PIVOT can be used to convert a table from a long format to a wide format. A long format table has one column for each variable and one row for each observation. A wide format table has one column for each variable and value combination and one row for each observation. PIVOT allows you to specify the column that contains the values to be pivoted, the column that contains the categories to be pivoted, and the aggregation function to be applied to the values. For example, the following query converts a long format table of sales data into a wide format table with columns for each product and sum of sales:SELECT *FROM salesPIVOT (SUM(sales_amount) FOR product IN (‘A’, ‘B’, ‘C’))References: The information can be referenced from Databricks documentation on SQL: PIVOT.https://files.training.databricks.com/assessments/practice-exams/PracticeExam-DataEngineerAssociate.pdfhttps://community.databricks.com/t5/data-engineering/practice-exams-for-databricks-certified-data-engineer/td-pNEW QUESTION 23A data engineer wants to create a new table containing the names of customers that live in France.They have written the following command:A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).Which of the following lines of code fills in the above blank to successfully complete the task?  There is no way to indicate whether a table contains PII.  “COMMENT PII”  TBLPROPERTIES PII  COMMENT “Contains PII”  PII In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it’s organization policy toindicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. References: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)NEW QUESTION 24Which 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]NEW QUESTION 25Which of the following commands will return the number of null values in the member_id column?  SELECT count(member_id) FROM my_table;  SELECT count(member_id) – count_null(member_id) FROM my_table;  SELECT count_if(member_id IS NULL) FROM my_table;  SELECT null(member_id) FROM my_table;  SELECT count_null(member_id) FROM my_table; To return the number of null values in the member_id column, the best option is to use the count_if function, which counts the number of rows that satisfy a given condition. In this case, the condition is that the member_id column is null. The other options are either incorrect or not supported by Spark SQL. Option A will return the number of non-null values in the member_id column. Option B will not work because there is no count_null function in Spark SQL. Option D will not work because there is no null function in Spark SQL.Option E will not work because there is no count_null function in Spark SQL. References:* Built-in Functions – Spark SQL, Built-in Functions* count_if – Spark SQL, Built-in FunctionsNEW QUESTION 26Which 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.References:* Databricks Architecture* Databricks Data Sources* Databricks Repos* [Databricks Notebooks]* [Databricks Data Engineer Professional Exam Guide]NEW QUESTION 27An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.Which of the following approaches can the manager use to ensure the results of the query are updated each day?  They can schedule the query to refresh every 1 day from the SQL endpoint’s page in Databricks SQL.  They can schedule the query to refresh every 12 hours from the SQL endpoint’s page in Databricks SQL.  They can schedule the query to refresh every 1 day from the query’s page in Databricks SQL.  They can schedule the query to run every 1 day from the Jobs UI.  They can schedule the query to run every 12 hours from the Jobs UI. NEW QUESTION 28Which of the following describes a scenario in which a data team will want to utilize cluster pools?  An automated report needs to be refreshed as quickly as possible.  An automated report needs to be made reproducible.  An automated report needs to be tested to identify errors.  An automated report needs to be version-controlled across multiple collaborators.  An automated report needs to be runnable by all stakeholders. Databricks cluster pools are a set of idle, ready-to-use instances that can reduce cluster start and auto-scaling times. This is useful for scenarios where a data team needs to run an automated report as quickly as possible, without waiting for the cluster to launch or scale up. Cluster pools can also help save costs by reusing idle instances across different clusters and avoiding DBU charges for idle instances in the pool. Reference: Best practices: pools | Databricks on AWS, Best practices: pools – Azure Databricks | Microsoft Learn, Best practices: pools | Databricks on Google CloudNEW QUESTION 29A 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”) Explanationhttps://spark.apache.org/docs/3.2.1/api/python/reference/api/pyspark.sql.SparkSession.table.htmlNEW QUESTION 30Which of the following describes the relationship between Gold tables and Silver tables?  Gold tables are more likely to contain aggregations than Silver tables.  Gold tables are more likely to contain valuable data than Silver tables.  Gold tables are more likely to contain a less refined view of data than Silver tables.  Gold tables are more likely to contain more data than Silver tables.  Gold tables are more likely to contain truthful data than Silver tables. According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer. They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables. Reference:What is the medallion lakehouse architecture?What is a Medallion Architecture?NEW QUESTION 31Which file format is used for storing Delta Lake Table?  Parquet  Delta  SV  JSON Delta Lake tables use the Parquet format as their underlying storage format. Delta Lake enhances Parquet by adding a transaction log that keeps track of all the operations performed on the table. This allows features like ACID transactions, scalable metadata handling, and schema enforcement, making it an ideal choice for big data processing and management in environments like Databricks.Reference:Databricks documentation on Delta Lake: Delta Lake OverviewNEW QUESTION 32Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?  Cloud-specific integrations  Simplified governance  Avoiding vendor lock-in  Ability to scale workloads  Ability to scale storage 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 DatabricksLakehouse 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). References: Databricks Documentation – Built on open source, Databricks Documentation – What is the Lakehouse Platform?, Databricks Blog – Introducing the Databricks Lakehouse Platform.NEW QUESTION 33A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.The table is configured to run in Production mode using the Continuous Pipeline Mode.Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?  All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.  All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.  All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.  All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.  All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing. ExplanationIn a Delta Live Table pipeline running in Continuous Pipeline Mode, when you click Start to update the pipeline, the following outcome is expected: All datasets defined using STREAMING LIVE TABLE and LIVE TABLE against Delta Lake table sources will be updated at set intervals. The compute resources will be deployed for the update process and will be active during the execution of the pipeline. The compute resources will be terminated when the pipeline is stopped or shut down. This mode allows for continuous and periodic updates to the datasets as new data arrives or changes in the underlying Delta Lake tables occur. The compute resources are provisioned and utilized during the update intervals to process the data and perform the necessary operations.NEW QUESTION 34A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?Which of the following code blocks can the data engineer use to complete this task?           https://www.w3schools.com/python/python_functions.asphttps://www.geeksforgeeks.org/python-functions/NEW QUESTION 35A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.Which of the following code blocks successfully completes this task?  Option A  Option B  Option C  Option D  Option E NEW QUESTION 36A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the notebooks is running slowly in the Job’s current run. The data engineer asks a tech lead for help in identifying why this might be the case.Which of the following approaches can the tech lead use to identify why the notebook is running slowly as part of the Job?  They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.  They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.  They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.  There is no way to determine why a Job task is running slowly.  They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook. NEW QUESTION 37Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?  SELECT * FROM my_table WHERE age > 25;  UPDATE my_table WHERE age > 25;  DELETE FROM my_table WHERE age > 25;  UPDATE my_table WHERE age <= 25;  DELETE FROM my_table WHERE age <= 25; The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table.This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. References: Table deletes, updates, and merges – Delta Lake DocumentationNEW QUESTION 38A 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 NEW QUESTION 39Which of the following describes the relationship between Bronze tables and raw data?  Bronze tables contain less data than raw data files.  Bronze tables contain more truthful data than raw data.  Bronze tables contain aggregates while raw data is unaggregated.  Bronze tables contain a less refined view of data than raw data.  Bronze tables contain raw data with a schema applied. ExplanationThe Bronze layer is where we land all the data from external source systems. The table structures in this layer correspond to the source system table structures “as-is,” along with any additional metadata columns that capture the load date/time, process ID, etc. The focus in this layer is quick Change Data Capture and the ability to provide an historical archive of source (cold storage), data lineage, auditability, reprocessing if needed without rereading the data from the source system.https://www.databricks.com/glossary/medallion-architecture#:~:text=Bronze%20layer%20%28raw%20dNEW QUESTION 40Which 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 DatabricksLakehouse 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). References: Databricks Documentation – Built on open source, Databricks Documentation – What is the Lakehouse Platform?, Databricks Blog – Introducing the Databricks Lakehouse Platform. Loading … Grab latest Amazon Databricks-Certified-Data-Engineer-Associate Dumps as PDF Updated: 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: 2024-09-28 11:26:00 Post date GMT: 2024-09-28 11:26:00 Post modified date: 2024-09-28 11:26:00 Post modified date GMT: 2024-09-28 11:26:00