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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.
NEW QUESTION # 36
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?
- A. They can set up an Alert with a custom template.
- B. They can set up an Alert with a new webhook alert destination.
- C. They can set up an Alert with one-time notifications.
- D. They can set up an Alert without notifications.
- E. They can set up an Alert with a new email alert destination.
Answer: B
NEW QUESTION # 37
A data engineer has realized that the data files associated with a Delta table are incredibly small. They want to compact the small files to form larger files to improve performance.
Which of the following keywords can be used to compact the small files?
- A. REDUCE
- B. COMPACTION
- C. VACUUM
- D. OPTIMIZE
- E. REPARTITION
Answer: D
Explanation:
Explanation
OPTIMIZE can be used to club small files into 1 and improve performance.
NEW QUESTION # 38
Which of the following describes the storage organization of a Delta table?
- A. Delta tables are stored in a single file that contains data, history, metadata, and other attributes.
- B. Delta tables are stored in a collection of files that contain only the data stored within the table.
- C. Delta tables store their data in a single file and all metadata in a collection of files in a separate location.
- D. Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.
- E. Delta tables are stored in a single file that contains only the data stored within the table.
Answer: D
NEW QUESTION # 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?
- A. This transfer is not possible
- B. New lead data engineer
- C. Databricks account representative
- D. Original data engineer
- E. Workspace administrator
Answer: E
Explanation:
Explanation
https://docs.databricks.com/sql/admin/transfer-ownership.html
NEW QUESTION # 40
Which of the following statements regarding the relationship between Silver tables and Bronze tables is always true?
- A. Silver tables contain less data than Bronze tables.
- B. Silver tables contain aggregates while Bronze data is unaggregated.
- C. Silver tables contain a more refined and cleaner view of data than Bronze tables.
- D. Silver tables contain more data than Bronze tables.
- E. Silver tables contain a less refined, less clean view of data than Bronze data.
Answer: C
Explanation:
Explanation
https://www.databricks.com/glossary/medallion-architecture
NEW QUESTION # 41
In which of the following file formats is data from Delta Lake tables primarily stored?
- A. JSON
- B. A proprietary, optimized format specific to Databricks
- C. CSV
- D. Parquet
- E. Delta
Answer: D
Explanation:
Explanation
https://docs.delta.io/latest/delta-faq.html
NEW QUESTION # 42
A 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?
- A. Option E
- B. Option B
- C. Option A
- D. Option D
- E. Option C
Answer: C
NEW QUESTION # 43
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?
- A. trigger(parallelBatch=True)
- B. trigger(availableNow=True)
- C. processingTime(1)
- D. trigger(continuous="once")
- E. trigger(processingTime="once")
Answer: B
Explanation:
Explanation
https://stackoverflow.com/questions/71061809/trigger-availablenow-for-delta-source-streaming-queries-in-pyspa
NEW QUESTION # 44
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?
- A. They can set up an Alert with a custom template.
- B. They can set up an Alert with a new webhook alert destination.
- C. They can set up an Alert with one-time notifications.
- D. They can set up an Alert without notifications.
- E. They can set up an Alert with a new email alert destination.
Answer: B
NEW QUESTION # 45
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 12 hours 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 1 day 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
Explanation:
Explanation
https://docs.databricks.com/en/sql/user/queries/schedule-query.html
NEW QUESTION # 46
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
- A. autoloader
- B. sqlite
- C. org.apache.spark.sql.jdbc
- D. org.apache.spark.sql.sqlite
- E. DELTA
Answer: C
Explanation:
Explanation
CREATE TABLE new_employees_table
USING JDBC
OPTIONS (
url "<jdbc_url>",
dbtable "<table_name>",
user '<username>',
password '<password>'
) AS
SELECT * FROM employees_table_vw
https://docs.databricks.com/external-data/jdbc.html#language-sql
NEW QUESTION # 47
A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.
Which of the following tools can the data engineer use to solve this problem?
- A. Auto Loader
- B. Delta Lake
- C. Delta Live Tables
- D. Unity Catalog
- E. Data Explorer
Answer: C
Explanation:
Explanation
https://docs.databricks.com/delta-live-tables/expectations.html
Delta Live Tables is a tool provided by Databricks that can help data engineers automate the monitoring of data quality. It is designed for managing data pipelines, monitoring data quality, and automating workflows.
With Delta Live Tables, you can set up data quality checks and alerts to detect issues and anomalies in your data as it is ingested and processed in real-time. It provides a way to ensure that the data quality meets your desired standards and can trigger actions or notifications when issues are detected. While the other tools mentioned may have their own purposes in a data engineeringenvironment, Delta Live Tables is specifically designed for data quality monitoring and automation within the Databricks ecosystem.
NEW QUESTION # 48
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?
- A. trigger(parallelBatch=True)
- B. trigger(availableNow=True)
- C. processingTime(1)
- D. trigger(continuous="once")
- E. trigger(processingTime="once")
Answer: B
Explanation:
Explanation
https://stackoverflow.com/questions/71061809/trigger-availablenow-for-delta-source-streaming-queries-in-pyspa
NEW QUESTION # 49
Which of the following benefits is provided by the array functions from Spark SQL?
- A. An ability to work with data in a variety of types at once
- B. An ability to work with data within certain partitions and windows
- C. An ability to work with time-related data in specified intervals
- D. An ability to work with complex, nested data ingested from JSON files
- E. An ability to work with an array of tables for procedural automation
Answer: B
NEW QUESTION # 50
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?
- A.
- B.
- C.
- D.
- E.
Answer: E
NEW QUESTION # 51
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?
- A.
- B.
- C.
- D.
- E.
Answer: C
NEW QUESTION # 52
Which of the following describes a scenario in which a data team will want to utilize cluster pools?
- A. An automated report needs to be made reproducible.
- B. An automated report needs to be refreshed as quickly as possible.
- C. An automated report needs to be version-controlled across multiple collaborators.
- D. An automated report needs to be tested to identify errors.
- E. An automated report needs to be runnable by all stakeholders.
Answer: B
Explanation:
Explanation
Cluster pools are typically used in distributed computing environments, such as cloud-based data platforms like Databricks. They allow you to pre-allocate a set of compute resources (a cluster) for specific tasks or workloads. In this case, if an automated report needs to be refreshed as quickly as possible, you can allocate a cluster pool with sufficient resources to ensure fast data processing and report generation. This helps ensure that the report is generated with minimal latency and can be delivered to stakeholders in a timely manner.
Cluster pools allow you to optimize resource allocation for high-demand, time-sensitive tasks like real-time report generation.
NEW QUESTION # 53
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
MERGE 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
INNER JOIN SELECT * FROM april_transactions; - D. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions; - E. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions;
Answer: E
NEW QUESTION # 54
A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.
Which of the following tools can the data engineer use to solve this problem?
- A. Auto Loader
- B. Unity Catalog
- C. Data Explorer
- D. Delta Lake
- E. Delta Live Tables
Answer: D
NEW QUESTION # 55
Which of the following tools is used by Auto Loader process data incrementally?
- A. Unity Catalog
- B. Checkpointing
- C. Data Explorer
- D. Spark Structured Streaming
- E. Databricks SQL
Answer: D
NEW QUESTION # 56
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 did not have a location
- C. The table's data was larger than 10 GB
- D. The table was external
- E. The table was managed
Answer: D
Explanation:
Explanation
The reason why the data files still exist while the metadata files were deleted is because the table was external.
When a table is external in Spark SQL (or in other database systems), it means that the table metadata (such as schema information and table structure) is managed externally, and Spark SQL assumes that the data is managed and maintained outside of the system. Therefore, when you execute a DROP TABLE statement for an external table, it removes only the table metadata from the catalog, leaving the data files intact. On the other hand, for managed tables (option E), Spark SQL manages both the metadata and the data files. When you drop a managed table, it deletes both the metadata and the associated data files, resulting in a complete removal of the table.
NEW QUESTION # 57
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
- A. The ability to set up alerts for query failures
- B. The ability to support batch and streaming workloads
- C. The ability to collaborate in real time on a single notebook
- D. The ability to distribute complex data operations
- E. The ability to manipulate the same data using a variety of languages
Answer: B
NEW QUESTION # 58
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
- A. The ability to set up alerts for query failures
- B. The ability to support batch and streaming workloads
- C. The ability to collaborate in real time on a single notebook
- D. The ability to distribute complex data operations
- E. The ability to manipulate the same data using a variety of languages
Answer: B
Explanation:
Explanation
Delta Lake is a key component of the Databricks Lakehouse Platform that provides several benefits, and one of the most significant benefits is its ability to support both batch and streaming workloads seamlessly. Delta Lake allows you to process and analyze data in real-time (streaming) as well as in batch, making it a versatile choice for various data processing needs. While the other options may be benefits or capabilities of Databricks or the Lakehouse Platform in general, they are not specifically associated with Delta Lake.
NEW QUESTION # 59
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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification program designed to recognize the skills and expertise of data engineering professionals. Databricks-Certified-Data-Engineer-Associate exam is intended for individuals who work with big data, data engineering, and distributed systems. It is a challenging exam that tests the candidate’s knowledge of data engineering concepts and practices.
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