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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming |
| Data Transformation, Cleansing, and Quality | 10% | - Implement schema evolution and management - Enforce data quality standards - Apply data cleansing and validation rules |
| Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Optimize compute and storage resources - Apply cost management best practices |
| Monitoring and Alerting | 10% | - Monitor pipeline performance and health - Track data lineage and metrics - Set up alerts and notifications |
| Data Modelling | 6% | - Optimize table design and partitioning - Implement dimensional and relational models - Design Medallion Architecture |
| Developing Code for Data Processing using Python and SQL | 22% | - Implement complex data processing logic - Write efficient and maintainable code - Use Databricks-specific libraries and APIs |
| Ensuring Data Security and Compliance | 10% | - Implement access control and permissions - Ensure data privacy and compliance - Secure data at rest and in transit |
| Data Sharing and Federation | 5% | - Manage cross-platform data access - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation |
| Data Governance | 7% | - Enforce data policies and standards - Use Unity Catalog for governance - Manage data assets and metadata |
| Debugging and Deploying | 10% | - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices |
1. In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone. A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?
A) Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
B) Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.
C) The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command
D) Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.
E) The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.
2. An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:
Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?
A) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
B) Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
C) Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
D) Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
E) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
3. Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?
A) /jobs/runs/list
B) /jobs/runs/get
C) /jobs/runs/get-output
D) /jobs/list
E) /jobs/get
4. The data governance team has instituted a requirement that the "user" table containing Personal Identifiable Information (PII) must have the appropriate masking on the SSN column. This means that anyone outside of the HRAdminGroup should see masked social security numbers as ***-**-
****.
The team created a masking function:
What does the data governance team need to do next to achieve this goal?
A) CREATE TABLE users
(name STRING, int STRING);
ALTER TABLE users ALTER COLUMN ssn CREATE MASK if is_member('HRAdminGroup');
B) CREATE TABLE users
(name STRING, ssn INT MASKED ssn_mask);
C) CREATE TABLE users
(name STRING, ssn STRING);
ALTER TABLE users ALTER COLUMN ssn SET MASK ssn_mask;
D) CREATE TABLE users
(name STRING);
ALTER TABLE users CREATE COLUMN ssn CREATE MASK ssn_mask;
5. A data engineering workspace was automatically enabled for Unity Catalog, creating a workspace catalog. New team members report they can create tables in the default schema but cannot access table in other schemas within the same workspace catalog. Why are the new team members unable to access tables in other schemas?
A) Workspace users receive USE CATALOG and specific privileges on default schema only.
B) Workspace catalog permissions are not subject to inheritance rules.
C) Tables in other schemas require additional BROWSEprivileges that new users don't receive automatically
D) New users only receive CREATE TABLE privileges on the default schema.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: E | Question # 4 Answer: C | Question # 5 Answer: A |
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