Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why Mount vs. URI**: Mounts provide a stable path (`/mnt/data`) and handle auth once; URIs (`s3://bucket/path`) require per-access credentials. For multi-workspace, Unity Catalog external locations replace mounts. **Steps (Azure ADLS)**: (1) Create service principal; grant...
This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Chubb. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (spark, window) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.
Why Mount vs. URI: Mounts provide a stable path (/mnt/data) and handle auth once; URIs (s3://bucket/path) require per-access credentials. For multi-workspace, Unity Catalog external locations replace mounts.
Steps (Azure ADLS): (1) Create service principal; grant Storage Blob Data Contributor. (2) Mount: dbutils.fs.mount(source="abfss://container@account.dfs.core.windows.net/", mount_point="/mnt/mydata", extra_configs={...}). (3) For key-based: use fs.azure.account.key.* in extra_configs.
AWS S3: Use IAM instance profile on cluster; s3a:// works without explicit keys. Or: assume role via spark.hadoop.fs.s3a.aws.credentials.provider.
Scalability Trade-offs: Mounts are per-workspace; init script ensures all clusters get them. Too many mounts slow startup.
Cost Implications: Use secret scopes (dbutils.secrets.get) for keys—never in notebooks. Migrate to Unity Catalog external locations for governance.
Pro-Move: 'Mount in init script; all clusters inherit. Secrets from Key Vault scope.' Red Flag: Hardcoding storage keys in notebook—security finding in audit.
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.