A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings .
The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

• A. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
• B. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
• C. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
• D. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
• E. The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.

說明：（僅 Fast2test 成員可見）

A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights.
Which privilege should be granted to the finance team lead?

• A. ALL PRIVILEGES on the finance_data catalog.
• B. GRANT OPTION privilege on the finance_data catalog.
• C. MANAGE privilege on the finance_data catalog.
• D. Make the finance team lead a metastore admin.

說明：（僅 Fast2test 成員可見）

The data science team has requested assistance in accelerating queries on free form text from user reviews.
The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?

• A. Delta Lake statistics are not optimized for free text fields with high cardinality.
• B. The Delta log creates a term matrix for free text fields to support selective filtering.
• C. Delta Lake statistics are only collected on the first 4 columns in a table.
• D. Text data cannot be stored with Delta Lake.
• E. ZORDER ON review will need to be run to see performance gains.

說明：（僅 Fast2test 成員可見）

Which statement describes Delta Lake Auto Compaction?

• A. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
• B. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
• C. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
• D. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
• E. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.

說明：（僅 Fast2test 成員可見）

A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders ?

• A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
• B. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
• C. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
• D. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
• E. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.