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Databricks Certified Professional Data Engineer Exam Sample Questions (Q107-Q112):

NEW QUESTION # 107
The data science team has created and logged a production using MLFlow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.

Which code block will output DataFrame with the schema'' customer_id LONG, predictions DOUBLE''?

A. Model, predict (df, columns)
B. Df. Select (''customer_id''.
Model (''columns) alias (''predictions'')
C. Df.apply(model, columns). Select (''customer_id, prediction''
D. Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')

Answer: A

Explanation:
Given the information that the model is registered with MLflow and assuming predict is the method used to apply the model to a set of columns, we use the model.predict() function to apply the model to the DataFrame df using the specified columns. The model.predict() function is designed to take in a DataFrame and a list of column names as arguments, applying the trained model to these features to produce a predictions column. When working with PySpark, this predictions column needs to be selected alongside the customer_id to create a new DataFrame with the schema customer_id LONG, predictions DOUBLE.
Reference:
MLflow documentation on using Python function models: https://www.mlflow.org/docs/latest/models.html#python-function-python PySpark MLlib documentation on model prediction: https://spark.apache.org/docs/latest/ml-pipeline.html#pipeline

NEW QUESTION # 108
In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone.
A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?

A. Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
B. The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.
C. The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command
D. Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.

Answer: C

Explanation:
In Delta Lake, a shallow clone creates a new table by copying the metadata of the source table without duplicating the data files. When the vacuum command is run on the source table, it removes old data files that are no longer needed to maintain the transactional log's integrity, potentially including files referenced by the shallow clone's metadata. If these files are purged, the shallow cloned tables will reference non-existent data files, causing them to stop working properly. This highlights the dependency of shallow clones on the source table's data files and the impact of data management operations like vacuum on these clones.
Reference: Databricks documentation on Delta Lake, particularly the sections on cloning tables (shallow and deep cloning) and data retention with the vacuum command (https://docs.databricks.com/delta/index.html).

NEW QUESTION # 109
Define an external SQL table by connecting to a local instance of an SQLite database using JDBC

A. 1.CREATE TABLE users_jdbc
2.USING org.apache.spark.sql.jdbc
3.OPTIONS (
4. url = "jdbc:sqlite:/sqmple_db",
5. dbtable = "users"
6.)
B. 1.CREATE TABLE users_jdbc
2.USING org.apache.spark.sql.jdbc.sqlite
3.OPTIONS (
4. url = "jdbc:/sqmple_db",
5. dbtable = "users"
6.)
C. 1.CREATE TABLE users_jdbc
2.USING SQLITE
3.OPTIONS (
4. url = "jdbc:/sqmple_db",
5. dbtable = "users"
6.)
D. 1.CREATE TABLE users_jdbc
2.USING SQL
3.OPTIONS (
4. url = "jdbc:sqlite:/sqmple_db",
5. dbtable = "users"
6.)
E. 1.CREATE TABLE users_jdbc
2.USING SQL
3.URL = {server:"jdbc:/sqmple_db",dbtable: "users"}

Answer: B

Explanation:
Explanation
The answer is,
1.CREATE TABLE users_jdbc
2.USING org.apache.spark.sql.jdbc
3.OPTIONS (
4. url = "jdbc:sqlite:/sqmple_db",
5. dbtable = "users"
6.)
Databricks runtime currently supports connecting to a few flavors of SQL Database including SQL Server, My SQL, SQL Lite and Snowflake using JDBC.
1.CREATE TABLE <jdbcTable>
2.USING org.apache.spark.sql.jdbc or JDBC
3.OPTIONS (
4. url = "jdbc:<databaseServerType>://<jdbcHostname>:<jdbcPort>",
5. dbtable " = <jdbcDatabase>.atable",
6. user = "<jdbcUsername>",
7. password = "<jdbcPassword>"
8.)
For more detailed documentation
SQL databases using JDBC - Azure Databricks | Microsoft Docs

NEW QUESTION # 110
A table is registered with the following code:

Bothusersandordersare Delta Lake tables. Which statement describes the results of queryingrecent_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 began.
B. 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.
C. 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.
D. 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.
E. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.

Answer: C

NEW QUESTION # 111
Which statement describes the default execution mode for Databricks Auto Loader?

A. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
B. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
C. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
D. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.

Answer: B

Explanation:
Databricks Auto Loader simplifies and automates the process of loading data into Delta Lake. The default execution mode of the Auto Loader identifies new files by listing the input directory. It incrementally and idempotently loads these new files into the target Delta Lake table. This approach ensures that files are not missed and are processed exactly once, avoiding data duplication. The other options describe different mechanisms or integrations that are not part of the default behavior of the Auto Loader.
Reference:
Databricks Auto Loader Documentation: Auto Loader Guide
Delta Lake and Auto Loader: Delta Lake Integration

NEW QUESTION # 112
......

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