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NEW QUESTION # 46
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/warehouse
- B. More information is needed to determine the correct response
- C. dbfs:/user/hive/database/customer360
- D. dbfs:/user/hive/customer360
Answer: A
Explanation:
Explanation
dbfs:/user/hive/warehouse - which is the default location
NEW QUESTION # 47
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 run every 1 day from the Jobs UI.
- B. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
- C. They can schedule the query to run every 12 hours from the Jobs UI.
- D. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
- E. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.
Answer: D
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. Reference: Schedule a query, What are Databricks SQL alerts?, Jobs.
NEW QUESTION # 48
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?
- A. When they are working interactively with a small amount of data
- B. When they are concerned about the ability to automatically scale with larger data
- C. When they are manually running reports with a large amount of data
- D. When they are working with SQL within Databricks SQL
- E. When they are running automated reports to be refreshed as quickly as possible
Answer: A
Explanation:
The scenario in which a data engineer will want to use a single-node cluster is when they are working interactively with a small amount of data. A single-node cluster is a cluster consisting of an Apache Spark driver and no Spark workers1. A single-node cluster supports Spark jobs and all Spark data sources, including Delta Lake1. A single-node cluster is helpful for single-node machine learning workloads that use Spark to load and save data, and for lightweight exploratory data analysis1. A single-node cluster can run Spark locally, spawn one executor thread per logical core in the cluster, and save all log output in the driver log1. A single-node cluster can be created by selecting the Single Node button when configuring a cluster1.
The other options are not suitable for using a single-node cluster. When running automated reports to be refreshed as quickly as possible, a data engineer will want to use a multi-node cluster that can scale up and down automatically based on the workload demand2. When working with SQL within Databricks SQL, a data engineer will want to use a SQL Endpoint that can execute SQL queries on a serverless pool or an existing cluster3. When concerned about the ability to automatically scale with larger data, a data engineer will want to use a multi-node cluster that can leverage the Databricks Lakehouse Platform and the Delta Engine to handle large-scale data processing efficiently and reliably4. When manually running reports with a large amount of data, a data engineer will want to use a multi-node cluster that can distribute the computation across multiple workers and leverage the Spark UI to monitor the performance and troubleshoot the issues.
Reference:
1: Single Node clusters | Databricks on AWS
2: Autoscaling | Databricks on AWS
3: SQL Endpoints | Databricks on AWS
4: Databricks Lakehouse Platform | Databricks on AWS
5: [Spark UI | Databricks on AWS]
NEW QUESTION # 49
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 PARQUET file format does not support COPY INTO.
- 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 COPY INTO statement requires the table to be refreshed to view the copied rows.
- E. The previous day's file has already been copied into the table.
Answer: E
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 # 50
Which of the following describes the relationship between Gold tables and Silver tables?
- A. Gold tables are more likely to contain more data than Silver tables.
- B. Gold tables are more likely to contain valuable data than Silver tables.
- C. Gold tables are more likely to contain a less refined view of data than Silver tables.
- D. Gold tables are more likely to contain aggregations than Silver tables.
- E. Gold tables are more likely to contain truthful data than Silver tables.
Answer: C
NEW QUESTION # 51
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 persist without any processing. The compute resources will persist but go unused.
- 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 shut down. The compute resources will be terminated.
- E. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
Answer: A
Explanation:
In Production mode, the pipeline runs continuously and updates the output tables whenever new data is available in the input sources. The compute resources are allocated on demand and released when the pipeline is stopped. This mode is suitable for production workloads that require high availability and reliability. References: Configure pipeline settings for Delta Live Tables, Tutorial: Run your first Delta Live Tables pipeline, Building Reliable Data Pipelines Using DataBricks' Delta Live Tables
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 schema(schema) with option ("maxFilesPerTrigger", 1)
- B. Replace "transactions" with the path to the location of the Delta table
- C. Replace predict with a stream-friendly prediction function
- D. Replace spark.read with spark.readStream
- E. Replace format("delta") with format("stream")
Answer: D
Explanation:
Explanation
https://docs.databricks.com/en/structured-streaming/delta-lake.html
In the context of Databricks, when transitioning from batch processing to stream processing, one common change that needs to be made is replacing spark.read with spark.readStream. This modification is essential because spark.read is used for batch processing, while spark.readStream is used for stream processing. The rest of the code can often remain the same or require minimal changes. References: The information can be referenced from Databricks documentation on structured streaming: Structured Streaming Programming Guide.
NEW QUESTION # 53
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?
- A. Both teams would be able to collaborate on projects in real-time
- B. Both teams would reorganize to report to the same department
- C. Both teams would use the same source of truth for their work
- D. Both teams would autoscale their work as data size evolves
- E. Both teams would respond more quickly to ad-hoc requests
Answer: C
Explanation:
Explanation
A data lakehouse is designed to unify the data engineering and data analysis architectures by integrating features of both data lakes and data warehouses. One of the key benefits of a data lakehouse is that it provides a common, centralized data repository (the "lake") that serves as a single source of truth for data storage and analysis. This allows both data engineering and data analysis teams to work with the same consistent data sets, reducing discrepancies and ensuring that the reports generated by both teams are based on the same underlying data.
NEW QUESTION # 54
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?
- A. They can turn on the Serverless feature for the SQL endpoint.
- B. They can increase the maximum bound of the SQL endpoint's scaling range
- C. They can turn on the Auto Stop feature for the SQL endpoint.
- D. They can increase the cluster size of the SQL endpoint.
- E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized."
Answer: A
Explanation:
Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non-running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms.
References: Databricks SQL Serverless, Serverless SQL endpoints, New Performance Improvements in Databricks SQL
NEW QUESTION # 55
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?
- A. UPDATE my_table WHERE age > 25;
- B. DELETE FROM my_table WHERE age <= 25;
- C. UPDATE my_table WHERE age <= 25;
- D. DELETE FROM my_table WHERE age > 25;
- E. SELECT * FROM my_table WHERE age > 25;
Answer: D
Explanation:
The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table.
This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. References: Table deletes, updates, and merges - Delta Lake Documentation
NEW QUESTION # 56
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. Replayable Sources and Idempotent Sinks
- B. Checkpointing and Idempotent Sinks
- C. Checkpointing and Write-ahead Logs
- D. Structured Streaming cannot record the offset range of the data being processed in each trigger.
- E. Write-ahead Logs and Idempotent Sinks
Answer: C
Explanation:
Explanation
The engine uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. -- in the link search for "The engine uses " youll find the answer.https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#:~:text=The%20engin
NEW QUESTION # 57
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
INNER JOIN SELECT * FROM april_transactions; - B. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions; - C. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions; - D. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions; - E. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
MERGE SELECT * FROM april_transactions;
Answer: B
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 # 58
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when it is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?
- A. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
- B. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
- C. They can turn on the Auto Stop feature for the SQL endpoint.
- D. They can reduce the cluster size of the SQL endpoint.
- E. They can set up the dashboard's SQL endpoint to be serverless.
Answer: C
Explanation:
The Auto Stop feature allows the SQL endpoint to automatically stop after a specified period of inactivity. This can help reduce the cost and resource consumption of the SQL endpoint, as it will only run when it is needed to refresh the dashboard or execute queries. The data engineer can configure the Auto Stop setting for the SQL endpoint from the SQL Endpoints UI, by selecting the desired idle time from the Auto Stop dropdown menu. The default idle time is 120 minutes, but it can be set to as low as 15 minutes or as high as 240 minutes. Alternatively, the data engineer can also use the SQL Endpoints REST API to set the Auto Stop setting programmatically. Reference: SQL Endpoints UI, SQL Endpoints REST API, Refreshing SQL Dashboard
NEW QUESTION # 59
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. datetime
- C. Cron syntax
- D. pyspark.sql.types.TimestampType
- E. There is no way to represent and submit this information programmatically
Answer: C
Explanation:
Cron syntax is a tool that can be used to represent and submit a complex run schedule programmatically. Cron syntax is a string of six fields that specify the frequency, date, and time of a job run. For example, the cron expression 0 0 12 * * ? means run the job at 12:00 PM every day. The data engineer can use the Databricks REST API to create or update a job with a cron schedule. The data engineer can also use the Databricks CLI to create or update a job with a cron schedule by using a JSON file that contains the cron expression. The other tools are either invalid or not suitable for representing and submitting a complex run schedule programmatically. References: Schedule a job, Jobs API, Databricks CLI, Cron expressions
NEW QUESTION # 60
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(continuous="once")
- B. trigger(processingTime="once")
- C. trigger(availableNow=True)
- D. trigger(parallelBatch=True)
- E. processingTime(1)
Answer: C
Explanation:
https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter
NEW QUESTION # 61
Which of the following Git operations must be performed outside of Databricks Repos?
- A. Commit
- B. Push
- C. Merge
- D. Pull
- E. Clone
Answer: E
NEW QUESTION # 62
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 B
- B. Option D
- C. Option A
- D. Option E
- E. Option C
Answer: D
NEW QUESTION # 63
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: E
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 # 64
......
To take the Databricks-Certified-Data-Engineer-Associate exam, individuals must have a good understanding of data engineering concepts and experience working with Databricks. Databricks-Certified-Data-Engineer-Associate exam consists of multiple-choice questions that cover a wide range of topics, including data ingestion, data transformation, data storage, and data analysis. Candidates must score a passing grade to earn the certification.
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