Question 1

When working with AUTO LOADER you noticed that most of the columns that were inferred as part of loading are string data types including columns that were supposed to be integers, how can we fix this?

Options :

A : Provide the schema of the source table in the cloudfiles.schemalocation

B : Provide the schema of the target table in the cloudfiles.schemalocation

C : Provide schema hints

D : Update the checkpoint location

E : Correct the incoming data by explicitly casting the data types

Answer: C

Question 2

A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

Options :

A : None of these changes will need to be made

B : The pipeline will need to stop using the medallion-based multi-hop architecture

C : The pipeline will need to be written entirely in SQL

D : The pipeline will need to use a batch source in place of a streaming source

E : The pipeline will need to be written entirely in Python

Answer: B

Question 3

You were asked to identify number of times a temperature sensor exceed threshold temperature (100.00) by each device, each row contains 5 readings collected every 5 minutes, fill in the blank with the appropriate functionsSchema: deviceId INT, deviceTemp ARRAY< double >, dateTimeCollected TIMESTAMP 2022-07-10-07-48-44-ce161b7b323528430beda1ec35cfd36e SELECT deviceId, __ (__ (__(deviceTemp], i -> i > 100.00)))FROM devicesGROUP BY deviceId

Options :

A : SUM, COUNT, SIZE

B : SUM, SIZE, SLICE

C : SUM, SIZE, ARRAY_CONTAINS

D : SUM, SIZE, ARRAY_FILTER

E : SUM, SIZE, FILTER

Answer: A

Question 4

When defining external tables using formats CSV, JSON, TEXT, BINARY any query on the external tables caches the data and location for performance reasons, so within a given spark session any new files that may have arrived will not be available after the initial query. How can we address this limitation?

Options :

A :

UNCACHE TABLE table_name

B : CACHE TABLE table_name

C : REFRESH TABLE table_name

D : BROADCAST TABLE table_name

E : CLEAR CACH table_name

Answer: C

Question 5

Which of the following python statement can be used to replace the schema name and table name in the query statement?

Options :

A : table_name = "sales"schema_name = "bronze"query = f”select * from schema_name.table_name”

B : table_name = "sales"schema_name = "bronze"query = "select * from {schema_name}.{table_name}"

C : table_name = "sales"schema_name = "bronze"query = f"select * from { schema_name}.{table_name}"

D : table_name = "sales"schema_name = "bronze"query = f"select * from + schema_name +"."+table_name"

Answer: C

Free Databricks-Certified-Associate-Data-Engineer Mock Exam – Practice Online Confidently

Buying Options: