DE-ASSOC practice questions 161–170

1. Question 161 · 170 in bank

   A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema. Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

   • AGRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
   • BGRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
   • CGRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;
   • DGRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;

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   Answer: A

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2. Question 162 · 170 in bank

   A data engineer has written a function in a Databricks Notebook to calculate the population of bacteria in a given medium.

   

   Analysts use this function in the notebook and sometimes provide input arguments of the wrong data type, which can cause errors during execution. Which Databricks feature will help the data engineer quickly identify if an incorrect data type has been provided as input?

   • AThe Spark User interface has a debug tab that contains the variables that are used in this session.
   • BThe Databricks debugger enables breakpoints that will raise an error if the wrong data type is submitted.
   • CThe Databricks debugger enables the use of a variable explorer to see at a glance the value of the variables.
   • DThe Data Engineer should add print statements to find out what the variable is.

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   Answer: C

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3. Question 163 · 170 in bank

   A data engineer needs to parse only png files in a directory that contains files with different suffixes. Which code should the data engineer use to achieve this task?

   • Adf = spark.readStream.format("cloudFiles") \ .option("cloudFiles.format", "binaryFile") \ .append("/*.png")
   • Bdf = spark.readstream. format("cloudFiles") \ .option("cloudFiles.format", "binaryFile") \ .option("pathGlobfilter", "*.png") \ .load()
   • Cdf = spark.readStream.format("cloudFiles") \ .option("cloudFiles.format", "binaryFile") \ .option("pathGlobfilter", "*.png") \ .append()
   • Ddf = spark.readstream.format("cloudFiles") \ .option("cloudFiles.format", "binaryFile") \ .load("/*.png")

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   Answer: B

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4. Question 164 · 170 in bank

   A data engineer is facing performance bottlenecks in an e-commerce transactions Delta table. The table is a managed Unity Catalog table, and it uses partitioning and Z-ordering in its data layout scheme. The predictive optimization for Unity Catalog tables is also enabled. The table has a frequently changing query filter, and the data engineer does not observe a benefit of the Data Layout or the Predictive Optimization. How should the data engineer fix the data layout bottlenecks?

   • ARe-write the Data Layout with Liquid Clustering and cluster by the Z-Ordered columns.
   • BEnable Delta Caching so that query results can be read through caches.
   • CTweak the Z-Order columns and run OPTIMIZE manually.
   • DSwitch the Data layout from Partition+Z-Ordering to Automatic Liquid Clustering.

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   Answer: D

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5. Question 165 · 170 in bank

   A data engineer needs to optimize the data layout and query performance for an e-commerce transactions Delta table. The table is partitioned by "purchase_date" a date column which helps with time-based queries but does not optimize searches on user statistics "customer_id", a high-cardinality column. The table is usually queried with filters on "customer_id" within specific date ranges, but since this data is spread across multiple files in each partition, it results in full partition scans and increased runtime and costs. How should the data engineer optimize the Data Layout for efficient reads?

   • AAlter table implementing liquid clustering on "customer_id" while keeping the existing partitioning.
   • BAlter the table implementing liquid clustering by "customer_id" and "purchase_date".
   • CAlter the table to partition by "customer_id".
   • DEnable delta caching on the cluster so that frequent reads are cached for performance.

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   Answer: B

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6. Question 166 · 170 in bank

   A data engineer works for an organization that must meet a stringent Service Level Agreement (SLA) that demands minimal runtime errors and high availability for its data processing pipelines. The data engineer wants to avoid the operational overhead of managing and tuning clusters. Which architectural solution will meet the requirements?

   • AUse an auto-scaling cluster configured and monitored by the user.
   • BImplement a hybrid approach with scheduled batch jobs on custom cloud VMs.
   • CDeploy a dedicated, manually managed cluster optimized by in-house IT staff.
   • DUtilize Databricks serverless compute that automatically optimizes resources and abstracts cluster management.

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   Answer: D

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7. Question 167 · 170 in bank

   An organization plans to share a large dataset stored in a Databricks workspace on AWS with a partner organization whose Databricks workspace is hosted on Azure. The data engineer wants to minimize data transfer costs while ensuring secure and efficient data sharing. Which strategy will reduce data egress costs associated with cross-cloud data sharing?

   • AMigrating the dataset to Cloudflare R2 object storage before sharing
   • BConfigure VPN connection between AWS and Azure for faster data sharing
   • CUsing Delta Sharing without any additional configurations
   • DSharing data via pre-signed URLs without monitoring egress costs

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   Answer: C

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8. Question 168 · 170 in bank

   A Databricks single-task workflow fails due to an error in a notebook. The data engineer fixes the mistake in the notebook. What should the data engineer do to rerun the workflow?

   • ARepair the task
   • BRepair the run
   • CRestart the Cluster
   • DSwap the cluster

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   Answer: A

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9. Question 169 · 170 in bank

   After a failed multi-task job run, a data engineer changes the cluster size and updates a notebook path for one task. The data engineer wants to re-run just the failed/skipped tasks with the updated settings and keep the run lineage intact. Which strategy should the data engineer use to preserve the run history?

   • AUse Jobs REST API repairRun to re-run failed tasks; the repair uses the current job and task settings and appears in the run history.
   • BIncrease the task retry count and click 'Retry' on the failed task to automatically apply new cluster settings within the same run.
   • CExport the job as a Databricks Asset Bundle, redeploy it, and re-run only the modified tasks from the bundle.
   • DUse 'Run now' to start a new run, so that the updated settings apply; the new run replaces the prior run history.

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   Answer: A

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10. Question 170 · 170 in bank

    A data engineer needs to enforce row-level security on main.secure.events(region STRING, event_id STRING). Members of the account group all_regions need to see all rows. All other users need to see only rows where region = 'EU'. Which SQL sequence satisfies the requirement?

    • A//IMG//
    • B//IMG//
    • C//IMG//
    • D//IMG//

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    Answer: A

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DE-ASSOC practice, question 161. 170 questions available.