DA-ASSOC practice questions 61–65

1. Question 61 · 65 in bank

   Where in the Databricks SQL workspace can a data analyst configure a refresh schedule for a query when the query is not attached to a dashboard or alert?

   • AData Explorer
   • BThe Visualization Editor
   • CThe Query Editor
   • DThe Dashboard Editor

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   Answer: C

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2. Question 62 · 65 in bank

   A data analyst filters rows where the tags array includes the value ‘sql’ using this query: SELECT * FROM main.analytics.articles WHERE tags = ‘sql’; This query returns no results. How should the analyst query to filter for rows where the tags array contains ‘sql’?

   • ASELECT * FROM main.analytics.articles WHERE tags IN (‘sql’);
   • BSELECT * FROM main.analytics.articles WHERE tags LIKE ‘%sql%’;
   • CSELECT * FROM main.analytics.articles WHERE ‘sql’ IN tags;
   • DSELECT * FROM main.analytics.articles WHERE array_conatins (tags, ‘sql’);

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   Answer: D

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3. Question 63 · 65 in bank

   A data analyst is troubleshooting a query in Databricks SQL that fails when processing large datasets and complex join operations. Logs indicate that the job consistently aborts due to resource constraint errors on the cluster. Which Query Profile metric should the analyst use to identify the operator that is causing resource overuse?

   • ATime spent per operator
   • BShuffle read size per operator
   • CMemory peak per operator
   • DBytes spilled to disk per operator

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   Answer: C

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4. Question 64 · 65 in bank

   A BI analyst is building an analytical data model in Databricks using Delta Lake tables. The source system contains transactional sales data that changes frequently. The analyst chooses to apply the Data Vault 2.0 methodology to manage historical changes while ensuring scalability and auditability across multiple business domains. Which component is used to capture the many-to-many relationship between hubs in a Data Vault v2 model?

   • AHub Table
   • BSatellite Table
   • CLink Table
   • DReference Table

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   Answer: C

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5. Question 65 · 65 in bank

   A data analyst team is building reliable, production-grade data pipelines on Databricks. They require a solution that allows them to define data transformations declaratively, automatically enforce data quality expectations, and handle errors without manual intervention. Which Databricks capability addresses these requirements?

   • ALakeflow Tables
   • BLakeflow Spark Declarative Pipelines
   • CLakeflow Jobs
   • DLakeflow Connect

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   Answer: B

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DA-ASSOC practice, question 61. 65 questions available.