Databricks-Certified-Data-Engineer-Associate Exam Dumps - Databricks Certified Data Engineer Associate Exam

To include a property indicating that a table contains personally identifiable information (PII), theTBLPROPERTIESkeyword is used in SQL to add metadata to a table. The correct syntax to define a table property for PII is as follows:

CREATE TABLE customersInFrance

USING DELTA

TBLPROPERTIES ('PII' = 'true')

AS

SELECT id,

firstName,

lastName

FROM customerLocations

WHERE country = 'FRANCE';

TheTBLPROPERTIES ('PII' = 'true')line correctly sets a table property that tags the table as containing personally identifiable information. This is in accordance with organizational policies for handling sensitive information.

A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. One of the key features of a data lakehouse is that it supports ACID-compliant transactions, which means that it ensures data integrity, consistency, and isolation across concurrent read and write operations3. This feature results in improved data quality over a traditional data lake, which does not support transactions and may suffer from data corruption, duplication, or inconsistency due to concurrent or streaming data ingestion and processing . References: 1: What is a Data Lakehouse? - Databricks 2: What is a Data Lakehouse? Definition, features & benefits. - Qlik 3: ACID Transactions - Databricks : [Data Lake vs Data Warehouse: Key Differences] : [Data Lakehouse: The Future of Data Engineering]

The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.forPath method to load the Delta table from its path2. References: 1: SparkSession | PySpark 3.2.0 documentation 2: Welcome to Delta Lake’s Python documentation page — delta-spark 2.4.0 documentation

 To return the number of null values in the member_id column, the best option is to use the count_if function, which counts the number of rows that satisfy a given condition. In this case, the condition is that the member_id column is null. The other options are either incorrect or not supported by Spark SQL. Option A will return the number of non-null values in the member_id column. Option B will not work because there is no count_null function in Spark SQL. Option D will not work because there is no null function in Spark SQL. Option E will not work because there is no count_null function in Spark SQL. References:

Built-in Functions - Spark SQL, Built-in Functions

count_if - Spark SQL, Built-in Functions

 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.

The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table. This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate,but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. References: Table deletes, updates, and merges — Delta Lake Documentation

In Python, the == operator is used to compare the values of two variables, while the = operator is used to assign a value to a variable. Therefore, option A and E are incorrect, as they use the = operator for comparison. Option B and C are also incorrect, as they compare the review_period variable to a string value "True", which is different from the booleanvalue True. Option D is the correct answer, as it uses the == operator to compare the day_of_week variable to the integer value 1, and the and operator to check if both conditions are true. If both conditions are true, then the final block of the Python program will be executed. References: [Python Operators], [Python If … Else]