Verified Databricks-Certified-Data-Engineer-Associate Dumps Q&As - Databricks-Certified-Data-Engineer-Associate Test Engine with Correct Answers
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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a highly sought-after certification in the field of data engineering. Databricks Certified Data Engineer Associate Exam certification is designed to validate the skills and knowledge required to design, build, and maintain data pipelines and data solutions on the Databricks platform. Databricks-Certified-Data-Engineer-Associate exam is intended for professionals who have a strong understanding of data engineering concepts and have experience working with Databricks.
NEW QUESTION # 22
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?
- A. When they are working interactively with a small amount of data
- B. When they are manually running reports with a large amount of data
- C. When they are working with SQL within Databricks SQL
- D. When they are running automated reports to be refreshed as quickly as possible
- E. When they are concerned about the ability to automatically scale with larger data
Answer: A
Explanation:
The scenario in which a data engineer will want to use a single-node cluster is when they are working interactively with a small amount of data. A single-node cluster is a cluster consisting of an Apache Spark driver and no Spark workers1. A single-node cluster supports Spark jobs and all Spark data sources, including Delta Lake1. A single-node cluster is helpful for single-node machine learning workloads that use Spark to load and save data, and for lightweight exploratory data analysis1. A single-node cluster can run Spark locally, spawn one executor thread per logical core in the cluster, and save all log output in the driver log1. A single-node cluster can be created by selecting the Single Node button when configuring a cluster1.
The other options are not suitable for using a single-node cluster. When running automated reports to be refreshed as quickly as possible, a data engineer will want to use a multi-node cluster that can scale up and down automatically based on the workload demand2. When working with SQL within Databricks SQL, a data engineer will want to use a SQL Endpoint that can execute SQL queries on a serverless pool or an existing cluster3. When concerned about the ability to automatically scale with larger data, a data engineer will want to use a multi-node cluster that can leverage the Databricks Lakehouse Platform and the Delta Engine to handle large-scale data processing efficiently and reliably4. When manually running reports with a large amount of data, a data engineer will want to use a multi-node cluster that can distribute the computation across multiple workers and leverage the Spark UI to monitor the performance and troubleshoot the issues.
Reference:
1: Single Node clusters | Databricks on AWS
2: Autoscaling | Databricks on AWS
3: SQL Endpoints | Databricks on AWS
4: Databricks Lakehouse Platform | Databricks on AWS
5: [Spark UI | Databricks on AWS]
NEW QUESTION # 23
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:
Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?
- A. Replace predict with a stream-friendly prediction function
- B. Replace spark.read with spark.readStream
- C. Replace "transactions" with the path to the location of the Delta table
- D. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
- E. Replace format("delta") with format("stream")
Answer: B
Explanation:
Explanation
https://docs.databricks.com/en/structured-streaming/delta-lake.html
NEW QUESTION # 24
A data engineer is attempting to drop a Spark SQL table my_table. The data engineer wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?
- A. The table's data was smaller than 10 GB
- B. The table was external
- C. The table was managed
- D. The table's data was larger than 10 GB
- E. The table did not have a location
Answer: B
Explanation:
An external table is a table that is defined in the metastore and points to an existing location in the storage system. When you drop an external table, only the metadata is deleted from the metastore, but the data files are not deleted from the storage system. This is because external tables are meant to be shared by multiple applications and users, and dropping them should not affect the data availability. On the other hand, a managed table is a table that is defined in the metastore and also managed by the metastore. When you drop a managed table, both the metadata and the data files are deleted from the metastore and the storage system, respectively. This is because managed tables are meant to be exclusive to the application or user that created them, and dropping them should free up the storage space. Therefore, the correct answer is C, because the table was external and only the metadata was deleted when the table was dropped. Reference: Databricks Documentation - Managed and External Tables, Databricks Documentation - Drop Table
NEW QUESTION # 25
An 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?
- A. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.
- B. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
- C. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
- D. They can schedule the query to run every 12 hours from the Jobs UI.
- E. They can schedule the query to run every 1 day from the Jobs UI.
Answer: B
NEW QUESTION # 26
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?
- A. Delta Lake
- B. Auto Loader
- C. Data Explorer
- D. Unity Catalog
- E. Databricks SQL
Answer: B
Explanation:
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 Tables
NEW QUESTION # 27
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?
- A. They can attach the cell to a SQL endpoint rather than a Databricks cluster
- B. It is not possible to use SQL in a Python notebook
- C. They can add %sql to the first line of the cell
- D. They can simply write SQL syntax in the cell
- E. They can change the default language of the notebook to SQL
Answer: C
Explanation:
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]
NEW QUESTION # 28
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?
- A. When another task needs to successfully complete before the new task begins
- B. When another task needs to use as little compute resources as possible
- C. When another task needs to fail before the new task begins
- D. When another task has the same dependency libraries as the new task
- E. When another task needs to be replaced by the new task
Answer: A
Explanation:
A data engineer can create a multi-task job in Databricks that consists of multiple tasks that run in a specific order. Each task can have one or more dependencies, which are other tasks that must run before the current task. The Depends On field of a new Databricks Job Task allows the data engineer to specify the dependencies of the task. The data engineer should select a task in the Depends On field when they want the new task to run only after the selected task has successfully completed. This can help the data engineer to create a logical sequence of tasks that depend on each other's outputs or results. For example, a data engineer can create a multi-task job that consists of the following tasks:
* Task A: Ingest data from a source using Auto Loader
* Task B: Transform the data using Spark SQL
* Task C: Write the data to a Delta Lake table
* Task D: Analyze the data using Spark ML
* Task E: Visualize the data using Databricks SQL
In this case, the data engineer can set the dependencies of each task as follows:
* Task A: No dependencies
* Task B: Depends on Task A
* Task C: Depends on Task B
* Task D: Depends on Task C
* Task E: Depends on Task D
This way, the data engineer can ensure that each task runs only after the previous task has successfully completed, and the data flows smoothly from ingestion to visualization.
The other options are incorrect because they do not describe valid scenarios for selecting a task in the Depends On field. The Depends On field does not affect the following aspects of a task:
* Whether the task needs to be replaced by another task
* Whether the task needs to fail before another task begins
* Whether the task has the same dependency libraries as another task
* Whether the task needs to use as little compute resources as possible References: Create a multi-task job, Run tasks conditionally in a Databricks job, Databricks Jobs.
NEW QUESTION # 29
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?
- A. They can set up separate expectations for each table when developing their DLT pipeline.
- B. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.
- C. They can set up DLT to notify them via email when records are dropped.
- D. They cannot determine which table is dropping the records.
- E. They can navigate to the DLT pipeline page, click on the "Error" button, and review the present errors.
Answer: B
Explanation:
Explanation
To identify the table in a Delta Live Tables (DLT) pipeline where data is being dropped due to quality concerns, the data engineer can navigate to the DLT pipeline page, click on each table in the pipeline, and view the data quality statistics. These statistics often include information about records dropped, violations of expectations, and other data quality metrics. By examining the data quality statistics for each table in the pipeline, the data engineer can determine at which table the data is being dropped.
NEW QUESTION # 30
A new data engineering team team. has been assigned to an ELT project. The new data engineering team will need full privileges on the database customers to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?
- A. GRANT USAGE ON DATABASE customers TO team;
- B. GRANT ALL PRIVILEGES ON DATABASE team TO customers;
- C. GRANT ALL PRIVILEGES ON DATABASE customers TO team;
- D. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;
- E. GRANT SELECT PRIVILEGES ON DATABASE customers TO teams;
Answer: C
Explanation:
To grant full permissions on a database to a user, group, or service principal, the GRANT ALL PRIVILEGES ON DATABASE command can be used. This command grants all the applicable privileges on the database, such as CREATE, SELECT, MODIFY, and USAGE. The other options are either incorrect or incomplete, as they do not grant all the privileges or specify the wrong database or principal. References:
* GRANT
* Privileges
NEW QUESTION # 31
Which of the following describes the relationship between Bronze tables and raw data?
- A. Bronze tables contain more truthful data than raw data.
- B. Bronze tables contain raw data with a schema applied.
- C. Bronze tables contain a less refined view of data than raw data.
- D. Bronze tables contain aggregates while raw data is unaggregated.
- E. Bronze tables contain less data than raw data files.
Answer: B
Explanation:
Bronze tables are the first layer of a medallion architecture, which is a data design pattern used to organize data in a lakehouse. Bronze tables contain raw data ingested from various sources, such as RDBMS data, JSON files, IoT data, etc. 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 only transformation applied to the raw data in this layer is to apply a schema, which defines the column names and data types of the table. The schema can be inferred from the data source or specified explicitly. Applying a schema to the raw data enables the use of SQL and other structured query languages to access and analyze the data. Therefore, option E is the correct answer. References: What is a Medallion Architecture?, Raw Data Ingestion into Delta Lake Bronze tables using Azure Synapse Mapping Data Flow, Apache Spark + Delta Lake concepts, Delta Lake Architecture & Azure Databricks Workspace.
NEW QUESTION # 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?
- A. if day_of_week = 1 & review_period: = "True":
- B. if day_of_week = 1 and review_period = "True":
- C. if day_of_week == 1 and review_period == "True":
- D. if day_of_week = 1 and review_period:
- E. if day_of_week == 1 and review_period:
Answer: E
Explanation:
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]
NEW QUESTION # 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?
- A. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.
- B. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.
- C. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.
- D. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.
- E. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.
Answer: B
Explanation:
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.
NEW QUESTION # 34
Which of the following describes the relationship between Bronze tables and raw data?
- A. Bronze tables contain raw data with a schema applied.
- B. Bronze tables contain more truthful data than raw data.
- C. Bronze tables contain a less refined view of data than raw data.
- D. Bronze tables contain aggregates while raw data is unaggregated.
- E. Bronze tables contain less data than raw data files.
Answer: D
NEW QUESTION # 35
A 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?
- A. 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.
- B. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
- C. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
- D. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
- E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
Answer: E
Explanation:
Explanation
In 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 # 36
A data engineer needs access to a table new_table, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which of the following approaches can be used to identify the owner of new_table?
- A. Review the Permissions tab in the table's page in Data Explorer
- B. All of these options can be used to identify the owner of the table
- C. There is no way to identify the owner of the table
- D. Review the Owner field in the table's page in Data Explorer
- E. Review the Owner field in the table's page in the cloud storage solution
Answer: D
Explanation:
he approach that can be used to identify the owner of new_table is to review the Owner field in the table's page in Data Explorer. Data Explorer is a web-based interface that allows users to browse, create, and manage data objects such as tables, views, and functions in Databricks1. The table's page in Data Explorer provides various information about the table, such as its schema, partitions, statistics, history, and permissions2. The Owner field shows the name and email address of the user who created or owns the table3. The data engineer can use this information to contact the table owner and request for permission to access the table.
The other options are not correct or reliable for identifying the owner of new_table. Reviewing the Permissions tab in the table's page in Data Explorer can show the users and groups who have access to the table, but not necessarily the owner4. Reviewing the Owner field in the table's page in the cloud storage solution can be misleading, as the owner of the data files may not be the same as the owner of the table5.
There is a way to identify the owner of the table, as explained above, so option E is false.
References:
* 1: Data Explorer | Databricks on AWS
* 2: Table details | Databricks on AWS
* 3: Set owner when creating a view in databricks sql - Databricks - 9978
* 4: Table access control | Databricks on AWS
* 5: External tables | Databricks on AWS
NEW QUESTION # 37
A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables.
Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?
- A. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions; - B. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions; - C. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions; - D. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
MERGE SELECT * FROM april_transactions; - E. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INNER JOIN SELECT * FROM april_transactions;
Answer: A
Explanation:
Explanation
To create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records, you should use the UNION operator, as shown in option B. This operator combines the result sets of the two tables while automatically removing duplicate records.
NEW QUESTION # 38
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?
- A. Data Explorer
- B. Repos
- C. Databricks Filesystem
- D. Dashboards
- E. Jobs
Answer: A
NEW QUESTION # 39
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Passing the Databricks Certified Data Engineer Associate certification exam is a great achievement that can enhance the career prospects of the candidates. Databricks Certified Data Engineer Associate Exam certification exam is recognized globally and is highly valued by the employers. Certified Data Engineer Associates can work as data engineers, data architects, data analysts, machine learning engineers, and data scientists in various industries such as healthcare, finance, retail, and technology. Databricks Certified Data Engineer Associate Exam certification exam is also a stepping stone towards the advanced Databricks Certified Data Engineer Professional certification exam.
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