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[2024] Databricks-Certified-Data-Engineer-Associate PDF Questions - Perfect Prospect To Go With ExamsLabs Practice Exam
Databricks-Certified-Data-Engineer-Associate certification exam is designed to test and validate the skills and knowledge of data engineers who work with Databricks. Databricks is a cloud-based data processing and analytics platform that allows companies to process and analyze large amounts of data in real-time. Databricks Certified Data Engineer Associate Exam certification exam covers a range of topics, including data engineering, data processing, and machine learning.
NEW QUESTION # 24
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
OUTER JOIN 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
UNION SELECT * FROM april_transactions; - E. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions;
Answer: D
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 # 25
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 SELECT PRIVILEGES ON DATABASE customers TO teams;
- B. GRANT ALL PRIVILEGES ON DATABASE customers TO team;
- C. GRANT ALL PRIVILEGES ON DATABASE team TO customers;
- D. GRANT USAGE ON DATABASE customers TO team;
- E. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;
Answer: B
Explanation:
Explanation
To grant full privileges on the database "customers" to the new data engineering team, you can use the GRANT ALL PRIVILEGES command as shown in option E. This command provides the team with all possible privileges on the specified database, allowing them to fully manage it.
NEW QUESTION # 26
A data engineer wants to create a data entity from a couple of tables. The data entity must be used by other data engineers in other sessions. It also must be saved to a physical location.
Which of the following data entities should the data engineer create?
- A. Function
- B. Table
- C. View
- D. Temporary view
- E. Database
Answer: B
Explanation:
Explanation
In the context described, creating a "Table" is the most suitable choice. Tables in SQL are data entities that exist independently of any session and are saved in a physical location. They can be accessed and manipulated by other data engineers in different sessions, which aligns with the requirements stated. A "Database" is a collection of tables, views, and other database objects. A "Function" is a stored procedure that performs an operation. A "View" is a virtual table based on the result-set of an SQL statement, but it is not stored physically. A "Temporary view" is a feature that allows you to store the result of a query as a view that disappears once your session with the database is closed.
NEW QUESTION # 27
Which of the following describes the relationship between Bronze tables and raw data?
- A. Bronze tables contain less data than raw data files.
- B. Bronze tables contain aggregates while raw data is unaggregated.
- C. Bronze tables contain more truthful data than raw data.
- D. Bronze tables contain raw data with a schema applied.
- E. Bronze tables contain a less refined view of data than raw data.
Answer: B
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 has the same dependency libraries as the new task
- C. When another task needs to be replaced by the new task
- D. When another task needs to fail before the new task begins
- E. When another task needs to use as little compute resources as possible
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
Which of the following data lakehouse features results in improved data quality over a traditional data lake?
- A. A data lakehouse supports ACID-compliant transactions.
- B. A data lakehouse enables machine learning and artificial Intelligence workloads.
- C. A data lakehouse stores data in open formats.
- D. A data lakehouse provides storage solutions for structured and unstructured data.
- E. A data lakehouse allows the use of SQL queries to examine data.
Answer: A
Explanation:
Explanation
One of the key features of a data lakehouse that results in improved data quality over a traditional data lake is its support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. ACID transactions provide data integrity and consistency guarantees, ensuring that operations on the data are reliable and that data is not left in an inconsistent state due to failures or concurrent access. In a traditional data lake, such transactional guarantees are often lacking, making it challenging to maintain data quality, especially in scenarios involving multiple data writes, updates, or complex transformations. A data lakehouse, by offering ACID compliance, helps maintain data quality by providing strong consistency and reliability, which is crucial for data pipelines and analytics.
NEW QUESTION # 30
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?
- A. Records that violate the expectation cause the job to fail.
- B. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
- C. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
- D. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
- E. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
Answer: A
Explanation:
The expected behavior when a batch of data containing data that violates the expectation is processed is that the job will fail. This is because the expectation clause has the ON VIOLATION FAIL UPDATE option, which means that if any record in the batch does not meet the expectation, the entire batch will be rejected and the job will fail. This option is useful for enforcing strict data quality rules and preventing invalid data from entering the target dataset.
Option A is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION DROP RECORD option should be used.
Option C is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and load them into a quarantine table, the ON VIOLATION QUARANTINE RECORD option should be used.
Option D is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION LOG RECORD option should be used.
Option E is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and flag them as invalid in a field added to the target dataset, the ON VIOLATION FLAG RECORD option should be used.
References:
* Delta Live Tables Expectations
* [Databricks Data Engineer Professional Exam Guide]
NEW QUESTION # 31
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?
- A. Checkpointing and Write-ahead Logs
- B. Write-ahead Logs and Idempotent Sinks
- C. Structured Streaming cannot record the offset range of the data being processed in each trigger.
- D. Checkpointing and Idempotent Sinks
- E. Replayable Sources and Idempotent Sinks
Answer: A
Explanation:
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 Semantics
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:
- C. if day_of_week = 1 and review_period:
- D. if day_of_week == 1 and review_period == "True":
- E. if day_of_week = 1 and review_period = "True":
Answer: B
Explanation:
Explanation
This 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.
NEW QUESTION # 33
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 without notifications.
- B. They can set up an Alert with a custom template.
- C. They can set up an Alert with a new email alert destination.
- D. They can set up an Alert with a new webhook alert destination.
- E. They can set up an Alert with one-time notifications.
Answer: D
NEW QUESTION # 34
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.
The data engineer runs the following query to join these tables together:
Which of the following will be returned by the above query?
- A. Option D
- B. Option A
- C. Option B
- D. Option E
- E. Option C
Answer: E
NEW QUESTION # 35
A 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?
- A. The VACUUM command was run on the table
- B. The OPTIMIZE command was nun on the table
- C. The TIME TRAVEL command was run on the table
- D. The DELETE HISTORY command was run on the table
- E. The HISTORY command was run on the table
Answer: A
Explanation:
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 # 36
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 was managed
- B. The table was external
- C. The table's data was smaller than 10 GB
- 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. References: Databricks Documentation - Managed and External Tables, Databricks Documentation - Drop Table
NEW QUESTION # 37
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: A
Explanation:
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 option
NEW QUESTION # 38
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?
- A. Write-ahead Logs and Idempotent Sinks
- B. Structured Streaming cannot record the offset range of the data being processed in each trigger.
- C. Checkpointing and Write-ahead Logs
- D. Replayable Sources and Idempotent Sinks
- E. Checkpointing and Idempotent Sinks
Answer: E
NEW QUESTION # 39
Which of the following is hosted completely in the control plane of the classic Databricks architecture?
- A. Driver node
- B. Databricks Filesystem
- C. JDBC data source
- D. Databricks web application
- E. Worker node
Answer: D
Explanation:
Explanation
In the classic Databricks architecture, the control plane includes components like the Databricks web application, the Databricks REST API, and the Databricks Workspace. These components are responsible for managing and controlling the Databricks environment, including cluster provisioning, notebook management, access control, and job scheduling. The other options, such as worker nodes, JDBC data sources, Databricks Filesystem (DBFS), and driver nodes, are typically part of the data plane or the execution environment, which is separate from the control plane. Worker nodes are responsible for executing tasks and computations, JDBC data sources are used to connect to external databases, DBFS is a distributed file system for data storage, and driver nodes are responsible for coordinating the execution of Spark jobs.
NEW QUESTION # 40
A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?
- A. Push
- B. Clone
- C. Pull
- D. Commit
- E. Merge
Answer: C
Explanation:
To sync a Databricks Repo with the changes from a central Git repository, the data engineer needs to run the Git pull operation. This operation fetches the latest updates from the remote repository and merges them with the local repository. The data engineer can use the Pull button in the Databricks Repos UI, or use the git pull command in a terminal session. The other options are not relevant for this task, as they either push changes to the remote repository (Push), combine two branches (Merge), save changes to the local repository (Commit), or create a new local repository from a remote one (Clone). References:
* Run Git operations on Databricks Repos
* Git pull
NEW QUESTION # 41
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 run every 12 hours from the Jobs UI.
- 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 1 day from the Jobs UI.
- E. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
Answer: E
Explanation:
Databricks SQL allows users to schedule queries to run automatically at a specified frequency and time zone.
This can help users to keep their dashboards or alerts updated with the latest data. To schedule a query, users need to do the following steps:
* In the Query Editor, click Schedule > Add schedule to open a menu with schedule settings.
* Choose when to run the query. Use the dropdown pickers to specify the frequency, period, starting time, and time zone. Optionally, select the Show cron syntax checkbox to edit the schedule in Quartz Cron Syntax.
* Choose More options to show optional settings. Users can also choose a name for the schedule, and a SQL warehouse to power the query.
* Click Create. The query will run automatically according to the schedule.
The other options are incorrect because they do not refer to the correct location or frequency to schedule the query. The query's page in Databricks SQL is the place where users can edit, run, or schedule the query. The SQL endpoint's page in Databricks SQL is the place where users can manage the SQL warehouses and SQL endpoints. The Jobs UI is the place where users can create, run, or schedule jobs that execute notebooks, JARs, or Python scripts. References: Schedule a query, What are Databricks SQL alerts?, Jobs.
NEW QUESTION # 42
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 persist without any processing. The compute resources will persist but go unused.
- D. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
- E. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
Answer: D
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 # 43
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?
- A. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
- B. They can clone the existing task to a new Job and then edit it to run the new notebook.
- C. They can create a new task in the existing Job and then add it as a dependency of the original task.
- D. They can clone the existing task in the existing Job and update it to run the new notebook.
- E. They can create a new job from scratch and add both tasks to run concurrently.
Answer: B
NEW QUESTION # 44
Which of the following Git operations must be performed outside of Databricks Repos?
- A. Push
- B. Clone
- C. Pull
- D. Commit
- E. Merge
Answer: E
Explanation:
Explanation
For following tasks, work in your Git provider:
Create a pull request.
Resolve merge conflicts.
Merge or delete branches.
Rebase a branch.
https://docs.databricks.com/repos/index.html
NEW QUESTION # 45
A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".
Today, the data engineer runs the following command to complete this task:
After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?
- A. The COPY INTO statement requires the table to be refreshed to view the copied rows.
- B. The previous day's file has already been copied into the table.
- C. The PARQUET file format does not support COPY INTO.
- D. The names of the files to be copied were not included with the FILES keyword.
- E. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.
Answer: B
Explanation:
Explanation
https://docs.databricks.com/en/ingestion/copy-into/index.html The COPY INTO SQL command lets you load data from a file location into a Delta table. This is a re-triable and idempotent operation; files in the source location that have already been loaded are skipped. if there are no new records, the only consistent choice is C no new files were loaded because already loaded files were skipped.
NEW QUESTION # 46
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?
- A. spark.delta.table
- B. SELECT * FROM sales
- C. spark.table
- D. spark.sql
- E. There is no way to share data between PySpark and SQL.
Answer: D
Explanation:
Explanation
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
df = spark.sql("SELECT * FROM sales")
print(df.count())
NEW QUESTION # 47
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Databricks-Certified-Data-Engineer-Associate certification is highly regarded in the industry and is recognized by many organizations as a mark of excellence in data engineering. Databricks Certified Data Engineer Associate Exam certification can help data engineers to advance their careers and increase their earning potential. It can also help employers to identify skilled and knowledgeable data engineers who can help them to unlock the value of their data.
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