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NEW QUESTION 30
Which of the following Structured Streaming queries is performing a hop from a Bronze table to a Silver
table?
- A. 1. (spark.table("sales")
2. .withColumn("avgPrice", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("append")
6. .table("cleanedSales")
7.) - B. 1. (spark.table("sales")
2. .groupBy("store")
3. .agg(sum("sales"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8.) - C. 1. (spark.readStream.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. ) - D. 1. (spark.read.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. ) - E. 1. (spark.table("sales")
2. .agg(sum("sales"),
3. sum("units"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8. )
Answer: A
NEW QUESTION 31
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 Development mode using the Triggered 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 at set intervals until the pipeline is shut down. The compute resources will
persist after the pipeline is stopped to allow for additional testing - C. All datasets will be updated continuously and the pipeline will not shut down. The compute resources
will persist with the pipeline - D. All datasets will be updated once and the pipeline will 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
NEW QUESTION 32
A data analyst has provided a data engineering team with the following Spark SQL query:
1.SELECT district,
2.avg(sales)
3.FROM store_sales_20220101
4.GROUP BY district;
The data analyst would like the data engineering team to run this query every day. The date at the end of the
table name (20220101) should automatically be replaced with the current date each time the query is run.
Which of the following approaches could be used by the data engineering team to efficiently auto-mate this
process?
- A. They could request that the data analyst rewrites the query to be run less frequently
- B. They could replace the string-formatted date in the table with a timestamp-formatted date
- C. They could manually replace the date within the table name with the current day's date
- D. They could wrap the query using PySpark and use Python's string variable system to automatically
update the table name - E. They could pass the table into PySpark and develop a robustly tested module on the existing query
Answer: D
NEW QUESTION 33
Two junior data engineers are authoring separate parts of a single data pipeline notebook. They are working on
separate Git branches so they can pair program on the same notebook simultaneously. A senior data engineer
experienced in Databricks suggests there is a better alternative for this type of collaboration.
Which of the following supports the senior data engineer's claim?
- A. Databricks Notebooks support the creation of interactive data visualizations
- B. Databricks Notebooks support the use of multiple languages in the same notebook
- C. Databricks Notebooks support commenting and notification comments
- D. Databricks Notebooks support real-time co-authoring on a single notebook
- E. Databricks Notebooks support automatic change-tracking and versioning
Answer: D
NEW QUESTION 34
A data engineer has three notebooks in an ELT pipeline. The notebooks need to be executed in a specific order
for the pipeline to complete successfully. The data engineer would like to use Delta Live Tables to manage this
process.
Which of the following steps must the data engineer take as part of implementing this pipeline using Delta
Live Tables?
- A. They need to refactor their notebook to use SQL and CREATE LIVE TABLE keyword
- B. They need to create a Delta Live tables pipeline from the Compute page
- C. They need to create a Delta Live Tables pipeline from the Jobs page
- D. They need to refactor their notebook to use Python and the dlt library
- E. They need to create a Delta Live Tables pipeline from the Data page
Answer: C
NEW QUESTION 35
......
