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NEW QUESTION 44
A data engineering team needs to query a Delta table to extract rows that all meet the same condi-tion.
However, the team has noticed that the query is running slowly. The team has already tuned the size of the
data files. Upon investigating, the team has concluded that the rows meeting the condition are sparsely located
throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query?

• A. Write as a Parquet file
• B. Z-Ordering
• C. Tuning the file size
• D. Data skipping
• E. Bin-packing

Answer: B

NEW QUESTION 45
A data engineering team has been using a Databricks SQL query to monitor the performance of an ELT job.
The ELT job is triggered by a specific number of input records being ready to process. The Databricks SQL
query returns the number of minutes since the job's most recent runtime.
Which of the following approaches can enable the data engineering team to be notified if the ELT job has not
been run in an hour?

• A. This type of alerting is not possible in Databricks
• B. They can set up an Alert for the accompanying dashboard to notify them if the returned value is greater
  than 60
• C. They can set up an Alert for the query to notify when the ELT job fails
• D. They can set up an Alert for the query to notify them if the returned value is greater than 60
• E. They can set up an Alert for the accompanying dashboard to notify when it has not re-freshed in 60
  minutes

Answer: D

NEW QUESTION 46
A junior data engineer needs to create a Spark SQL table my_table for which Spark manages both the data and
the metadata. The metadata and data should also be stored in the Databricks Filesystem (DBFS).
Which of the following commands should a senior data engineer share with the junior data engineer to
complete this task?

• A. 1. CREATE MANAGED TABLE my_table (id STRING, value STRING) USING
  2. org.apache.spark.sql.parquet OPTIONS (PATH "storage-path");
• B. 1. CREATE MANAGED TABLE my_table (id STRING, value STRING);
• C. 1. CREATE TABLE my_table (id STRING, value STRING) USING
  2. org.apache.spark.sql.parquet OPTIONS (PATH "storage-path")
• D. 1. CREATE TABLE my_table (id STRING, value STRING) USING DBFS;
• E. 1. CREATE TABLE my_table (id STRING, value STRING);

Answer: E

NEW QUESTION 47
Which of the following describes a benefit of a data lakehouse that is unavailable in a traditional data
warehouse?

• A. A data lakehouse provides a relational system of data management
• B. A data lakehouse utilizes proprietary storage formats for data
• C. A data lakehouse couples storage and compute for complete control
• D. A data lakehouse enables both batch and streaming analytics
• E. A data lakehouse captures snapshots of data for version control purposes

Answer: D

NEW QUESTION 48
A data engineer has ingested a JSON file into a table raw_table with the following schema:
1.transaction_id STRING,
2.payload ARRAY<customer_id:STRING, date:TIMESTAMP, store_id:STRING>
The data engineer wants to efficiently extract the date of each transaction into a table with the fol-lowing
schema:
1.transaction_id STRING,
2.date TIMESTAMP
Which of the following commands should the data engineer run to complete this task?

• A. 1.SELECT transaction_id, explode(payload)
  2.FROM raw_table;
• B. 1.SELECT transaction_id, date from payload
  2.FROM raw_table;
• C. 1.SELECT transaction_id, payload.date
  2.FROM raw_table;
• D. 1.SELECT transaction_id, payload[date]
  2.FROM raw_table;
• E. 1.SELECT transaction_id, date
  2.FROM raw_table;

Answer: C

NEW QUESTION 49
......