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Databricks Certified Data Engineer Associate exam measures an individual's ability to design and implement data pipelines, optimize and tune big data solutions, and manage data workflows using Databricks. Candidates are expected to have a solid understanding of data processing techniques, data storage, and data management concepts. Databricks Certified Data Engineer Associate Exam certification program offers a comprehensive assessment of an individual's skills in the field of big data engineering and provides a valuable credential for those looking to advance their careers in this field.
NEW QUESTION # 55
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 more truthful data than raw data.
- C. Bronze tables contain raw data with a schema applied.
- D. Bronze tables contain aggregates while raw data is unaggregated.
- E. Bronze tables contain a less refined view of data than raw data.
Answer: D
NEW QUESTION # 56
A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?
- A. dbfs:/user/hive/customer360
- B. More information is needed to determine the correct response
- C. dbfs:/user/hive/warehouse
- D. dbfs:/user/hive/database/customer360
Answer: C
Explanation:
Explanation
dbfs:/user/hive/warehouse - which is the default location
NEW QUESTION # 57
Which of the following Git operations must be performed outside of Databricks Repos?
- A. Push
- B. Commit
- C. Clone
- D. Merge
- E. Pull
Answer: D
Explanation:
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as commit, pull, push, branch management, and visual comparison of diffs when committing1. However, merge is not supported in the Git dialog2. You need to use the Repos UI or your Git provider to merge branches3. Merge is a way to combine the commit history from one branch into another branch1. During a merge, a merge conflict is encountered when Git cannot automatically combine code from one branch into another. Merge conflicts require manual resolution before a merge can be completed1. Reference: 4: Run Git operations on Databricks Repos4, 1: CI/CD techniques with Git and Databricks Repos1, 3: Collaborate in Repos3, 2: Databricks Repos - What it is and how we can use it2.
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as commit, pull, push, merge, and branch management. However, to clone a remote Git repository to a Databricks repo, you must use the Databricks UI or API. You cannot clone a Git repo using the CLI through a cluster's web terminal, as the files won't display in the Databricks UI1. Reference: 1: Run Git operations on Databricks Repos | Databricks on AWS2
NEW QUESTION # 58
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 more data than Bronze tables.
- C. Silver tables contain a less refined, less clean view of data than Bronze data.
- D. Silver tables contain aggregates while Bronze data is unaggregated.
- E. Silver tables contain a more refined and cleaner view of data than Bronze tables.
Answer: B
Explanation:
Explanation
https://www.databricks.com/glossary/medallion-architecture
NEW QUESTION # 59
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. Databricks account representative
- B. This transfer is not possible
- C. Original data engineer
- D. New lead data engineer
- E. Workspace administrator
Answer: E
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 # 60
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?
- A. Databricks Repos automatically saves development progress
- B. Databricks Repos supports the use of multiple branches
- C. Databricks Repos provides the ability to comment on specific changes
- D. Databricks Repos is wholly housed within the Databricks Lakehouse Platform
- E. Databricks Repos allows users to revert to previous versions of a notebook
Answer: B
Explanation:
Explanation
An advantage of using Databricks Repos over the built-in Databricks Notebooks versioning is the ability to work with multiple branches. Branching is a fundamental feature ofversion control systems like Git, which Databricks Repos is built upon. It allows you to create separate branches for different tasks, features, or experiments within your project. This separation helps in parallel development and experimentation without affecting the main branch or the work of other team members. Branching provides a more organized and collaborative development environment, making it easier to merge changes and manage different development efforts. While Databricks Notebooks versioning also allows you to track versions of notebooks, it may not provide the same level of flexibility and collaboration as branching in Databricks Repos.
NEW QUESTION # 61
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 format of the files to be copied were not included with the FORMAT_OPTIONS keyword.
- C. The names of the files to be copied were not included with the FILES keyword.
- D. The previous day's file has already been copied into the table.
- E. The PARQUET file format does not support COPY INTO.
Answer: D
Explanation:
The COPY INTO statement is an idempotent operation, which means that it will skip any files that have already been loaded into the target table1. This ensures that the data is not duplicated or corrupted by multiple attempts to load the same file. Therefore, if the data engineer runs the same command every day without specifying the names of the files to be copied with the FILES keyword or a glob pattern with the PATTERN keyword, the statement will only copy the first file that matches the source location and ignore the rest. To avoid this problem, the data engineer should either use the FILES or PATTERN keywords to filter the files to be copied based on the date or some other criteria, or delete the files from the source location after they are copied into the table2. References: 1: COPY INTO | Databricks on AWS 2: Get started using COPY INTO to load data | Databricks on AWS
NEW QUESTION # 62
In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?
- A. When the location of the data needs to be changed
- B. When the target table cannot contain duplicate records
- C. When the target table is an external table
- D. When the source table can be deleted
- E. When the source is not a Delta table
Answer: B
Explanation:
The MERGE INTO command is used to perform upserts, which are a combination of insertions and updates, based on a source table into a target Delta table1. The MERGE INTO command can handle scenarios where the target table cannot contain duplicate records, such as when there is a primary key or a unique constraint on the target table. The MERGE INTO command can match the source and target rows based on a merge condition and perform different actions depending on whether the rows are matched or not. For example, the MERGE INTO command can update the existing target rows with the new source values, insert the new source rows that do not exist in the target table, or delete the target rows that do not exist in the source table1.
The INSERT INTO command is used to append new rows to an existing table or create a new table from a query result2. The INSERT INTO command does not perform any updates or deletions on the existing target table rows. The INSERT INTO command can handle scenarios where the location of the data needs to be changed, such as when the data needs to be moved from one table to another, or when the data needs to be partitioned by a certain column2. The INSERT INTO command can also handle scenarios where the target table is an external table, such as when the data is stored in an external storage system like Amazon S3 or Azure Blob Storage3. The INSERT INTO command can also handle scenarios where the source table can be deleted, such as when the source table is a temporary table or a view4. The INSERT INTO command can also handle scenarios where the source is not a Delta table, such as when the source is a Parquet, CSV, JSON, or Avro file5.
References:
* 1: MERGE INTO | Databricks on AWS
* 2: [INSERT INTO | Databricks on AWS]
* 3: [External tables | Databricks on AWS]
* 4: [Temporary views | Databricks on AWS]
* 5: [Data sources | Databricks on AWS]
NEW QUESTION # 63
A 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?
- A. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.
- B. There is no way to determine why a Job task is running slowly.
- C. They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.
- D. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.
- E. They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.
Answer: C
Explanation:
The Tasks tab in the Jobs UI shows the list of tasks that are part of a job, and allows the user to view the details of each task, such as the notebook path, the cluster configuration, the run status, and the duration. By clicking on the active run of a task, the user can access the Spark UI, the notebook output, and the logs of the task. These can help the user to identify the performance bottlenecks and errors in the task. The Runs tab in the Jobs UI only shows the summary of the job runs, such as the start time, the end time, the trigger, and the status. It does not provide the details of the individual tasks within a job run. References: Jobs UI, Monitor running jobs with a Job Run dashboard, How to optimize jobs performance
NEW QUESTION # 64
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 one-time notifications.
- C. They can set up an Alert with a new webhook alert destination.
- D. They can set up an Alert with a new email alert destination.
- E. They can set up an Alert without notifications.
Answer: C
Explanation:
A webhook alert destination is a way to send notifications to external applications or services via HTTP requests. A data engineer can use a webhook alert destination to notify their entire team via a messaging webhook, such as Slack or Microsoft Teams, whenever the number of NULL values in the input data reaches
100. To set up a webhook alert destination, the data engineer needs to do the following steps:
* In the Databricks SQL workspace, navigate to the Settings gear icon and select SQL Admin Console.
* Click Alert Destinations and click Add New Alert Destination.
* Select Webhook and enter the webhook URL and the optional custom template for the notification message.
* Click Create to save the webhook alert destination.
* In the Databricks SQL editor, create or open the query that returns the number of input records containing unexpected NULL values.
* Click the Create Alert icon above the editor window and configure the alert criteria, such as the value column, the condition, and the threshold.
* In the Notification section, select the webhook alert destination that was created earlier and click Create Alert. References: What are Databricks SQL alerts?, Monitor alerts, Monitoring Your Business with Alerts, Using Automation Runbook Webhooks To Alert on Databricks Status Updates.
NEW QUESTION # 65
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 Idempotent Sinks
- B. Structured Streaming cannot record the offset range of the data being processed in each trigger.
- C. Write-ahead Logs and Idempotent Sinks
- D. Replayable Sources and Idempotent Sinks
- E. Checkpointing and Write-ahead Logs
Answer: E
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 # 66
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 C
- B. Option D
- C. Option E
- D. Option B
- E. Option A
Answer: E
Explanation:
Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns: customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.
NEW QUESTION # 67
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. org.apache.spark.sql.sqlite
- B. autoloader
- C. DELTA
- D. sqlite
- E. org.apache.spark.sql.jdbc
Answer: D
Explanation:
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. References:
* Create a table using JDBC
* JDBC connection string
* SQLite JDBC driver
NEW QUESTION # 68
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?
- A. The pipeline will need to stop using the medallion-based multi-hop architecture
- B. The pipeline will need to be written entirely in Python
- C. None of these changes will need to be made
- D. The pipeline will need to use a batch source in place of a streaming source
- E. The pipeline will need to be written entirely in SQL
Answer: C
NEW QUESTION # 69
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 once and the pipeline will shut down. The compute resources will be terminated.
- B. 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.
- C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
- 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 once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
Answer: B
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 # 70
Which of the following commands will return the number of null values in the member_id column?
- A. SELECT count(member_id) - count_null(member_id) FROM my_table;
- B. SELECT count_null(member_id) FROM my_table;
- C. SELECT null(member_id) FROM my_table;
- D. SELECT count(member_id) FROM my_table;
- E. SELECT count_if(member_id IS NULL) FROM my_table;
Answer: E
Explanation:
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 Functions
NEW QUESTION # 71
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. View
- C. Temporary view
- D. Table
- E. Database
Answer: D
Explanation:
A table is a data entity that is stored in a physical location and can be accessed by other data engineers in other sessions. A table can be created from one or more tables using the CREATE TABLE or CREATE TABLE AS SELECT commands. A table can also be registered from an existing DataFrame using the spark.catalog.createTable method. A table can be queried using SQL or DataFrame APIs. A table can also be updated, deleted, or appended using the MERGE INTO command or the DeltaTable API. References:
* Create a table
* Create a table from a query result
* Register a table from a DataFrame
* [Query a table]
* [Update, delete, or merge into a table]
NEW QUESTION # 72
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 one-time notifications.
- C. They can set up an Alert with a new webhook alert destination.
- D. They can set up an Alert with a new email alert destination.
- E. They can set up an Alert without notifications.
Answer: C
Explanation:
Explanation
To achieve this, the data engineer can set up an Alert in the Databricks workspace that triggers when the query results exceed the threshold of 100 NULL values. They can create a new webhook alert destination in the Alert's configuration settings and provide the necessary messaging webhook URL to receive notifications.
When the Alert is triggered, it will send a message to the configured webhook URL, which will then notify the entire team of the issue.
NEW QUESTION # 73
Which of the following describes the relationship between Gold tables and Silver tables?
- A. Gold tables are more likely to contain aggregations than Silver tables.
- B. Gold tables are more likely to contain valuable data than Silver tables.
- C. Gold tables are more likely to contain truthful data than Silver tables.
- D. Gold tables are more likely to contain a less refined view of data than Silver tables.
- E. Gold tables are more likely to contain more data than Silver tables.
Answer: A
Explanation:
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. References:
* What is the medallion lakehouse architecture?
* What is a Medallion Architecture?
NEW QUESTION # 74
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(processingTime="once")
- B. trigger(parallelBatch=True)
- C. processingTime(1)
- D. trigger(continuous="once")
- E. trigger(availableNow=True)
Answer: E
Explanation:
Explanation
https://stackoverflow.com/questions/71061809/trigger-availablenow-for-delta-source-streaming-queries-in-pyspa
NEW QUESTION # 75
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 a more refined and cleaner view of data than Bronze tables.
- C. Silver tables contain a less refined, less clean view of data than Bronze data.
- D. Silver tables contain more data than Bronze tables.
- E. Silver tables contain aggregates while Bronze data is unaggregated.
Answer: B
Explanation:
Explanation
https://www.databricks.com/glossary/medallion-architecture
NEW QUESTION # 76
Which of the following data lakehouse features results in improved data quality over a traditional data lake?
- A. A data lakehouse provides storage solutions for structured and unstructured data.
- B. A data lakehouse stores data in open formats.
- C. A data lakehouse enables machine learning and artificial Intelligence workloads.
- D. A data lakehouse supports ACID-compliant transactions.
- E. A data lakehouse allows the use of SQL queries to examine data.
Answer: D
Explanation:
A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. One of the key features of a data lakehouse is that it supports ACID-compliant transactions, which means that it ensures data integrity, consistency, and isolation across concurrent read and write operations3. This feature results in improved data quality over a traditional data lake, which does not support transactions and may suffer from data corruption, duplication, or inconsistency due to concurrent or streaming data ingestion and processing . Reference: 1: What is a Data Lakehouse? - Databricks 2: What is a Data Lakehouse? Definition, features & benefits. - Qlik 3: ACID Transactions - Databricks : [Data Lake vs Data Warehouse: Key Differences] : [Data Lakehouse: The Future of Data Engineering]
NEW QUESTION # 77
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 CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;
- B. GRANT ALL PRIVILEGES ON DATABASE team TO customers;
- C. GRANT SELECT PRIVILEGES ON DATABASE customers TO teams;
- D. GRANT ALL PRIVILEGES ON DATABASE customers TO team;
- E. GRANT USAGE ON DATABASE customers TO team;
Answer: D
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. Reference:
GRANT
Privileges
NEW QUESTION # 78
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. Delta Lake
- B. Unity Catalog
- C. Delta Live Tables
- D. Auto Loader
- E. Data Explorer
Answer: C
Explanation:
Delta Live Tables is a tool that enables data engineers to build and manage reliable data pipelines with minimal code. One of the features of Delta Live Tables is data quality monitoring, which allows data engineers to define quality expectations for their data and automatically check them at every step of the pipeline. Data quality monitoring can help detect and resolve data quality issues, such as missing values, duplicates, outliers, or schema changes. Data quality monitoring can also generate alerts and reports on the quality level of the data, and enable data engineers to troubleshoot and fix problems quickly. Reference: Delta Live Tables Overview, Data Quality Monitoring
NEW QUESTION # 79
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