Databricks Certified Data Engineer Associate - Databricks-Certified-Data-Engineer-Associate Free Exam Questions

A. Use separate Git branches for each environment so each branch can contain environment-specific configuration values.

B. Define bundle variables and reference them from bundle resources, and then override the variable values for each environment using bundle targets.

C. Deploy the same bundle once and edit job settings in the workspace UI for each environment after deployment.

D. Parameterize environment-specific values inside notebooks and set those parameters whenever each job run is triggered.

A. Unity Catalog

B. Databricks SQL

C. Spark Structured Streaming

D. Checkpointing

A. WHERE customer_id = :cid

B. WHERE customer_id <=> :cid

C. WHERE COALESCE(customer_id, '') = COALESCE(:cid, '')

D. WHERE customer_id <> :cid OR customer_id IS NULL

A. They could only run the entire program on Sundays.

B. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.

C. They could submit a feature request with Databricks to add this functionality.

D. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.

E. They could redesign the data model to separate the data used in the final query into a new table.

A. Gold tables are more likely to contain a less refined view of data than Silver tables.

B. Gold tables are more likely to contain truthful data than Silver tables.

C. Gold tables are more likely to contain valuable data than Silver tables.

D. Gold tables are more likely to contain more data than Silver tables.

E. Gold tables are more likely to contain aggregations than Silver tables.

A. Use a non-incremental Snowflake connector, fetch all data every four hours, and apply MERGE operations.

B. Use Structured Streaming for Kafka and a Lakeflow Connect connector with a full refresh for Snowflake.

C. Use Structured Streaming for Kafka and a Lakeflow Connect managed connector with incremental processing for Snowflake.

D. Use spark.readStream() with Kafka and query Snowflake hourly using a time-based filter.

A. Use a Delta table with its native schema enforcement, schema evolution, and table-history capabilities.

B. Use an external table with Auto Loader schema inference for the CSV files.

C. Use a Parquet table with Spark's default schema inference and rerun the job whenever the schema changes.

D. Use a Delta table with schema enforcement and recreate the table whenever new columns are added.

A. DataOps

B. GitHub

C. Databricks Asset Bundles

D. Terraform

A. An ability to work with data within certain partitions and windows

B. An ability to work with an array of tables for procedural automation

C. An ability to work with complex, nested data ingested from JSON files

D. An ability to work with data in a variety of types at once

E. An ability to work with time-related data in specified intervals

A. Overwrite the target Delta table during every run.

B. Recreate the target table before every run.

C. Load corrections as new files and apply updates to the target table.

D. Reload all files from the source path during every run.

A. Set the dashboard task to depend only on the SQL query task; the other tasks run independently.

B. Create separate Lakeflow Jobs for each parallel task and trigger them sequentially using external orchestration.

C. Add the dashboard task as dependent on all three parallel tasks using fan-in control flow.

D. Configure the parallel tasks with sequential dependencies so that each task waits for the previous task to finish.

A. Error Handling

B. Monitoring

C. Expectations

D. Change Data Capture

A. Use Lakeflow Connect managed connectors for Salesforce and SQL Server, and Auto Loader for the ADLS files, within Lakeflow Spark Declarative Pipelines

B. Use Lakeflow Connect managed connectors for all three sources

C. Use partner connectors such as Fivetran for all three sources through Partner Connect

D. Use Auto Loader for all three sources, with custom Python code for Salesforce API calls and SQL Server CDC extraction

A. CREATE TABLE all_transactions ASSELECT * FROM march_transactionsMERGE SELECT * FROM april_transactions;

B. CREATE TABLE all_transactions ASSELECT * FROM march_transactionsINTERSECT SELECT * from april_transactions;

C. CREATE TABLE all_transactions ASSELECT * FROM march_transactionsINNER JOIN SELECT * FROM april_transactions;

D. CREATE TABLE all_transactions ASSELECT * FROM march_transactionsUNION SELECT * FROM april_transactions;

E. CREATE TABLE all_transactions ASSELECT * FROM march_transactionsOUTER JOIN SELECT * FROM april_transactions;