[Apr 17, 2025] Get to the Top with Databricks-Certified-Data-Engineer-Associate Practice Exam Questions
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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) certification exam is an excellent opportunity for data professionals to validate their skills in designing, building, and maintaining data pipelines using Databricks. Databricks Certified Data Engineer Associate Exam certification exam covers a wide range of topics and is designed to test the candidate's knowledge of data engineering fundamentals, Databricks architecture, data modeling, and data processing. Passing the certification exam provides an individual with a globally recognized certification that can enhance their career prospects in the data engineering field.
Databricks Certified Data Engineer Associate Exam is designed for data engineers, software developers, and IT professionals who work with data on a regular basis. Databricks Certified Data Engineer Associate Exam certification program is ideal for individuals who want to demonstrate their expertise in designing and building data pipelines, working with big data, and developing data-driven applications using Databricks.
NEW QUESTION # 41
A data engineer has a Job that has a complex run schedule, and they want to transfer that schedule to other Jobs.
Rather than manually selecting each value in the scheduling form in Databricks, which of the following tools can the data engineer use to represent and submit the schedule programmatically?
- A. pyspark.sql.types.DateType
- B. Cron syntax
- C. pyspark.sql.types.TimestampType
- D. There is no way to represent and submit this information programmatically
- E. datetime
Answer: B
NEW QUESTION # 42
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day.
They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?
- A. They could submit a feature request with Databricks to add this functionality.
- B. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
- C. They could redesign the data model to separate the data used in the final query into a new table.
- D. They could only run the entire program on Sundays.
- E. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
Answer: E
NEW QUESTION # 43
A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?
- A. Option C
- B. Option E
- C. Option B
- D. Option D
- E. Option A
Answer: B
NEW QUESTION # 44
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. Unity Catalog
- B. Auto Loader
- C. Databricks SQL
- D. Data Explorer
- E. Delta Lake
Answer: B
Explanation:
Explanation
Auto Loader incrementally and efficiently processes new data files as they arrive in cloud storage without any additional setup.https://docs.databricks.com/en/ingestion/auto-loader/index.html
NEW QUESTION # 45
Which of the following is hosted completely in the control plane of the classic Databricks architecture?
- A. Driver node
- B. JDBC data source
- C. Databricks web application
- D. Databricks Filesystem
- E. Worker node
Answer: A
NEW QUESTION # 46
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. New lead data engineer
- B. This transfer is not possible
- C. Workspace administrator
- D. Original data engineer
- E. Databricks account representative
Answer: C
Explanation:
The workspace administrator is the only individual who can transfer ownership of the Delta tables in Data Explorer, assuming the original data engineer no longer has access. The workspace administrator has the highest level of permissions in the workspace and can manage all resources, users, and groups. The other options are either not possible or not sufficient to perform the ownership transfer. The Databricks account representative is not involved in the workspace management. The transfer is possible and not dependent on the original data engineer. The new lead data engineer may not have the necessary permissions to access or modify the Delta tables, unless granted by the workspace administrator or the original data engineer before leaving. Reference: Workspace access control, Manage Unity Catalog object ownership.
NEW QUESTION # 47
In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?
- A. When the target table cannot contain duplicate records
- B. When the source table can be deleted
- C. When the location of the data needs to be changed
- D. When the target table is an external table
- E. When the source is not a Delta table
Answer: A
Explanation:
Explanation
With merge , you can avoid inserting the duplicate records. The dataset containing the new logs needs to be deduplicated within itself. By the SQL semantics of merge, it matches and deduplicates the new data with the existing data in the table, but if there is duplicate data within the new dataset, it is inserted.https://docs.databricks.com/en/delta/merge.html#:~:text=With%20merge%20%2C%20you%20can%20a
NEW QUESTION # 48
A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id -
FROM STREAM(LIVE.customers)
WHERE loyalty_level = 'high';
Which of the following describes why the STREAM function is included in the query?
- A. The customers table is a reference to a Structured Streaming query on a PySpark DataFrame.
- B. The STREAM function is not needed and will cause an error.
- C. The data in the customers table has been updated since its last run.
- D. The customers table is a streaming live table.
- E. The table being created is a live table.
Answer: D
Explanation:
The STREAM function is used to process data from a streaming live table or view, which is a table or view that contains data that has been added only since the last pipeline update. Streaming live tables and views are stateful, meaning that they retain the state of the previous pipeline run and only process new data based on the current query. This is useful for incremental processing of streaming or batch data sources. The customers table in the query is a streaming live table, which means that it contains the latest data from the source. The STREAM function enables the query to read the data from the customers table incrementally and create another streaming live table named loyal_customers, which contains the customer IDs of the customers with high loyalty level. Reference: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.
NEW QUESTION # 49
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?
- A. spark.sql("sales")
- B. spark.delta.table("sales")
- C. SELECT * FROM sales
- D. There is no way to share data between PySpark and SQL.
- E. spark.table("sales")
Answer: E
NEW QUESTION # 50
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?
- A. Parquet files can be partitioned
- B. Parquet files have the ability to be optimized
- C. Parquet files have a well-defined schema
- D. Parquet files will become Delta tables
- E. CREATE TABLE AS SELECT statements cannot be used on files
Answer: C
Explanation:
Option C is the correct answer because Parquet files have a well-defined schema that is embedded within the data itself. This means that the data types and column names of the Parquet files are automatically detected and preserved when creating an external table from them. This also enables the use of SQL and other structured query languages to access and analyze the data. CSV files, on the other hand, do not have a schema embedded in them, and require specifying the schema explicitly or inferring it from the data when creating an external table from them. This can lead to errors or inconsistencies in the data types and column names, and also increase the processing time and complexity.
References: CREATE TABLE AS SELECT, Parquet Files, CSV Files, Parquet vs. CSV
NEW QUESTION # 51
A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?
- A. Option C
- B. Option E
- C. Option B
- D. Option D
- E. Option A
Answer: B
NEW QUESTION # 52
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 format("delta") with format("stream")
- B. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
- C. Replace spark.read with spark.readStream
- D. Replace "transactions" with the path to the location of the Delta table
- E. Replace predict with a stream-friendly prediction function
Answer: C
Explanation:
Explanation
https://docs.databricks.com/en/structured-streaming/delta-lake.html
NEW QUESTION # 53
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?
- A. trigger("5 seconds")
- B. trigger()
- C. trigger(continuous="5 seconds")
- D. trigger(processingTime="5 seconds")
- E. trigger(once="5 seconds")
Answer: D
Explanation:
The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). References: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.
NEW QUESTION # 54
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 run every 1 day from the Jobs UI.
- D. They can schedule the query to run every 12 hours from the Jobs UI.
- E. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
Answer: B
Explanation:
Explanation
https://docs.databricks.com/en/sql/user/queries/schedule-query.html
NEW QUESTION # 55
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. processingTime(1)
- B. trigger(parallelBatch=True)
- C. trigger(continuous="once")
- D. trigger(processingTime="once")
- E. trigger(availableNow=True)
Answer: E
Explanation:
https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter.trigger.html
NEW QUESTION # 56
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
INNER JOIN SELECT * FROM april_transactions; - C. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION 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
INTERSECT SELECT * from april_transactions;
Answer: C
Explanation:
The correct command to create a new table that contains all records from two tables without duplicate records is to use the UNION operator. The UNION operator combines the results of two queries and removes any duplicate rows. The INNER JOIN, OUTER JOIN, and MERGE operators do not remove duplicate rows, and the INTERSECT operator only returns the rows that are common to both tables. Therefore, option B is the only correct answer. References: Databricks SQL Reference - UNION, Databricks SQL Reference - JOIN, Databricks SQL Reference - MERGE, [Databricks SQL Reference - INTERSECT]
NEW QUESTION # 57
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