Certified-Data-Engineer-Professional試験無料問題集「Databricks Certified Data Engineer Professional 認定」

A. The total count of rows is calculated by scanning all data files

B. The total count of records is calculated from the parquet file metadata

C. The total count of records is calculated from the Delta transaction logs

D. The total count of rows will be returned from cached results unless REFRESH is run

E. The total count of records is calculated from the Hive metastore

A. libraries

B. jobs

C. fs

D. workspace

E. configure

A. 

B. 

C. 

D. 

A. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.

B. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.

C. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.

D. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.

E. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.

A. Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.

B. Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.

C. Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.

D. Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.

A. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.

B. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.

C. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

D. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.

E. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

A. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.

B. Periodically run OPTIMIZE table_name.

C. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.

D. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.

E. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.

A. Increase the shuffle partitions to account for additional aggregates

B. Remove .option (mergeSchema', true') from the streaming write

C. Register the data in the "/item_agg" directory to the Hive metastore

D. Specify a new checkpointlocation

E. Run REFRESH TABLE delta, /item_agg'

A. Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.

B. Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.

C. With Open Sharing, recipients cannot access Volumes, Models, or notebooks -- only static Delta tables are supported.

D. With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.

A. There is a type error because a DataFrame object cannot be multiplied.

B. There is a type error because a column object cannot be multiplied.

C. There is no column in the table named heartrateheartrateheartrate

D. There is a syntax error because the heartrate column is not correctly identified as a column.

E. The code executed was PvSoark but was executed in a Scala notebook.

A. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading < 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")

B. @dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")

C. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")

D. @dlt.table
@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
@dlt.table
@dlt.expect_or_drop("invalid_sensor_reading", "reading >= 120")
def quarantine_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")

A. Use a service principal ID and its OAuth client secret.

B. Use a service principal and its Personal Access Token.

C. Use a shared user account and its OAuth client secret.

D. Use a service principal with OAuth token federation.

A. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.

B. The checkpoint directory will be used to track updates to the static Delta table.

C. Stream-static joins cannot use static Delta tables because of consistency issues.

D. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.

E. The checkpoint directory will be used to track state information for the unique keys present in the join.

A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.

B. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.

C. Both commands will fail. No new variables, tables, or views will be created.

D. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.

E. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.