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NEW QUESTION # 50
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?
- A.
- B.
- C.
- D.
- E.
Answer: B
Explanation:
Explanation
https://www.w3schools.com/python/python_functions.asp
NEW QUESTION # 51
A data engineer has been given a new record of data:
id STRING = 'a1'
rank INTEGER = 6
rating FLOAT = 9.4
Which of the following SQL commands can be used to append the new record to an existing Delta table my_table?
- A. UPDATE VALUES ('a1', 6, 9.4) my_table
- B. my_table UNION VALUES ('a1', 6, 9.4)
- C. UPDATE my_table VALUES ('a1', 6, 9.4)
- D. INSERT INTO my_table VALUES ('a1', 6, 9.4)
- E. INSERT VALUES ( 'a1' , 6, 9.4) INTO my_table
Answer: D
NEW QUESTION # 52
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 SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;
- D. GRANT USAGE ON DATABASE customers TO team;
- E. GRANT ALL PRIVILEGES ON DATABASE team TO customers;
Answer: B
NEW QUESTION # 53
A data engineer has realized that the data files associated with a Delta table are incredibly small. They want to compact the small files to form larger files to improve performance.
Which of the following keywords can be used to compact the small files?
- A. VACUUM
- B. REDUCE
- C. OPTIMIZE
- D. REPARTITION
- E. COMPACTION
Answer: C
Explanation:
The keyword that can be used to compact the small files associated with a Delta table is OPTIMIZE. The OPTIMIZE command performs file compaction on a Delta table by rewriting a set of small files into a set of larger files1. This can improve the performance of queries that scan the table by reducing the number of files that need to be read and the amount of metadata that needs to be processed1. The OPTIMIZE command can also optionally sort the data within each file by a given set of columns, which can further improve the query performance by enabling data skipping and predicate pushdown1. The OPTIMIZE command can be applied to the whole table or to a specific partition of the table1.
The other keywords are not suitable for compacting the small files associated with a Delta table. REDUCE is a keyword used in the SQL syntax for aggregating data using a user-defined function2. COMPACTION is not a valid keyword in SQL or Python. REPARTITION is a keyword used in the Python syntax for changing the number of partitions of a DataFrame or an RDD3. VACUUM is a keyword used to remove files that are no longer referenced by a Delta table and are older than a retention threshold4.
References:
* 1: OPTIMIZE | Databricks on AWS
* 2: REDUCE | Databricks on AWS
* 3: repartition | Databricks on AWS
* 4: VACUUM | Databricks on AWS
NEW QUESTION # 54
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. View
- B. Table
- C. Temporary view
- D. Database
- E. Function
Answer: B
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 # 55
A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?
- A. spark.delta.sql
- B. spark.sql
- C. spark.delta.table
- D. dbutils.sql
- E. spark.table
Answer: B
NEW QUESTION # 56
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. There is no way to represent and submit this information programmatically
- B. Cron syntax
- C. pyspark.sql.types.TimestampType
- D. pyspark.sql.types.DateType
- E. datetime
Answer: B
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 # 57
A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?
- A. GRANT CREATE ON DATABASE customers TO team;
- B. GRANT USAGE ON CATALOG team TO customers;
- C. GRANT VIEW ON CATALOG customers TO team;
- D. GRANT USAGE ON DATABASE customers TO team;
- E. GRANT CREATE ON DATABASE team TO customers;
Answer: D
Explanation:
The correct command to grant the necessary permission on the entire database to the new team is to use the GRANT USAGE command. The GRANT USAGE command grants the principal the ability to access the securable object, such as a database, schema, or table. In this case, the securable object is the database customers, and the principal is the group team. By granting usage on the database, the team will be able to see what tables already exist in the database. Option E is the only option that uses the correct syntax and the correct privilege type for this scenario. Option A uses the wrong privilege type (VIEW) and the wrong securable object (CATALOG). Option B uses the wrong privilege type (CREATE), which would allow the team to create new tables in the database, but not necessarily see the existing ones. Option C uses the wrong securable object (CATALOG) and the wrong principal (customers). Option D uses the wrong securable object (team) and the wrong principal (customers). References: GRANT, Privilege types, Securable objects, Principals
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 is not one of the included query's SQL endpoint.
- B. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
- 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. References: SQL Endpoints UI, SQL Endpoints REST API, Refreshing SQL Dashboard
NEW QUESTION # 59
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 query's page in Databricks SQL.
- 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 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 1 day from the SQL endpoint's page in Databricks SQL.
Answer: A
NEW QUESTION # 60
A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?
- A. GRANT CREATE ON DATABASE customers TO team;
- B. GRANT USAGE ON CATALOG team TO customers;
- C. GRANT VIEW ON CATALOG customers TO team;
- D. GRANT USAGE ON DATABASE customers TO team;
Answer: D
NEW QUESTION # 61
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 have the ability to be optimized
- B. Parquet files will become Delta tables
- C. Parquet files can be partitioned
- D. Parquet files have a well-defined schema
- E. CREATE TABLE AS SELECT statements cannot be used on files
Answer: D
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 # 62
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 # 63
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(processingTime="5 seconds")
- C. trigger(once="5 seconds")
- D. trigger()
- E. trigger(continuous="5 seconds")
Answer: B
Explanation:
Explanation
# ProcessingTime trigger with two-seconds micro-batch interval
df.writeStream \
format("console") \
trigger(processingTime='2 seconds') \
start()
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#triggers
NEW QUESTION # 64
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 fail before the new task begins
- B. When another task has the same dependency libraries as the new task
- C. When another task needs to use as little compute resources as possible
- D. When another task needs to be replaced by the new task
- E. When another task needs to successfully complete before the new task begins
Answer: E
NEW QUESTION # 65
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. Delta Lake
- C. Auto Loader
- D. Databricks SQL
- E. Data Explorer
Answer: C
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 # 66
Which of the following is stored in the Databricks customer's cloud account?
- A. Cluster management metadata
- B. Databricks web application
- C. Notebooks
- D. Data
- E. Repos
Answer: D
Explanation:
The only option that is stored in the Databricks customer's cloud account is data. Data is stored in the customer's cloud storage service, such as AWS S3 or Azure Data Lake Storage. The customer has full control and ownership of their data and can access it directly from their cloud account.
Option A is not correct, as the Databricks web application is hosted and managed by Databricks on their own cloud infrastructure. The customer does not need to install or maintain the web application, but only needs to access it through a web browser.
Option B is not correct, as the cluster management metadata is stored and managed by Databricks on their own cloud infrastructure. The cluster management metadata includes information such as cluster configuration, status, logs, and metrics. The customer can view and manage their clusters through the Databricks web application, but does not have direct access to the cluster management metadata.
Option C is not correct, as the repos are stored and managed by Databricks on their own cloud infrastructure.
Repos are version-controlled repositories that store code and data files for Databricks projects. The customer can create and manage their repos through the Databricks web application, but does not have direct access to the repos.
Option E is not correct, as the notebooks are stored and managed by Databricks on their own cloud infrastructure. Notebooks are interactive documents that contain code, text, and visualizations for Databricks workflows. The customer can create and manage their notebooks through the Databricks web application, but does not have direct access to the notebooks.
References:
* Databricks Architecture
* Databricks Data Sources
* Databricks Repos
* [Databricks Notebooks]
* [Databricks Data Engineer Professional Exam Guide]
NEW QUESTION # 67
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 names of the files to be copied were not included with the FILES keyword.
- C. The previous day's file has already been copied into the table.
- D. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.
- E. The COPY INTO statement requires the table to be refreshed to view the copied rows.
Answer: C
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 # 68
A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?
- A. spark.delta.sql
- B. spark.sql
- C. spark.delta.table
- D. dbutils.sql
- E. spark.table
Answer: B
Explanation:
The spark.sql method can be used to execute SQL queries programmatically and return the result as a DataFrame. The spark.sql method accepts a string argument that contains a valid SQL statement. The data engineer can use a formatted string literal (f-string) to insert the Python variable table_name into the SQL query. The other methods are either invalid or not suitable for running SQL queries. References: Running SQL Queries Programmatically, Formatted string literals, spark.sql
NEW QUESTION # 69
Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?
- A. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.
- B. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.
- C. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.
- D. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.
- E. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.
Answer: B
Explanation:
A streaming live table or view processes data that has been added only since the last pipeline update.
Streaming tables and views are stateful; if the defining query changes, new data will be processed based on the new query and existing data is not recomputed. This is useful when data needs to be processed incrementally, such as when ingesting streaming data sources or performing incremental loads from batch data sources. A live table or view, on the other hand, may be entirely computed when possible to optimize computation resources and time. This is suitable when data needs to be processed in full, such as when performing complex transformations or aggregations that require scanning all the data. References: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.
NEW QUESTION # 70
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 dropped from the target dataset and loaded into a quarantine table.
- C. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
- 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 flagged as invalid in a field added to the target dataset.
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 # 71
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. Replayable Sources and Idempotent Sinks
- D. Checkpointing and Idempotent Sinks
- E. Structured Streaming cannot record the offset range of the data being processed in each trigger.
Answer: D
NEW QUESTION # 72
......
Databricks Certified Data Engineer Associate certification is recognized globally and is highly valued by employers in the data engineering industry. Becoming certified as a Databricks data engineer associate demonstrates that an individual has the necessary skills and knowledge to work with Databricks technologies and can apply them to real-world scenarios. Databricks Certified Data Engineer Associate Exam certification also helps individuals stand out in a highly competitive job market, as it serves as a testament to their commitment to professional development.
Databricks-Certified-Data-Engineer-Associate Exam tests the candidates' abilities to perform the necessary tasks needed to become a successful data engineer. Databricks-Certified-Data-Engineer-Associate exam covers a wide range of topics such as data extraction, transformation, and loading (ETL), data modeling, data warehousing, and machine learning. Individuals who have passed Databricks-Certified-Data-Engineer-Associate exam possess the professional knowledge necessary to integrate, manipulate, secure, and monitor data within Databricks.
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