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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution |
| Monitoring and Alerting | 10% | - Setting up alerts and notifications - Pipeline observability and logging - Performance and health monitoring |
| Debugging and Deploying | 10% | - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices |
| Developing Code for Data Processing using Python and SQL | 22% | - Data transformation and aggregation - Integration with Databricks APIs and tools - Batch and incremental processing logic |
| Data Modelling | 6% | - Delta Lake table design - Medallion Architecture implementation - Schema design and management |
| Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Handling missing or inconsistent data - Standardization and normalization |
| Data Governance | 7% | - Policy enforcement - Unity Catalog management - Data lineage and metadata tracking |
| Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Ensuring Data Security and Compliance | 10% | - Compliance standards implementation - Access control and permissions - Data encryption and masking |
| Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching - Cluster configuration and scaling |
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NEW QUESTION # 21
A new data engineer new.engineer@company.com has been assigned to an ELT project. The new data
engineer will need full privileges on the table sales to fully manage the project.
Which of the following commands can be used to grant full permissions on the table to the new data engineer?
Answer: C
NEW QUESTION # 22
The data engineering team maintains the following code:
Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?
Answer: I
Explanation:
Writes Data to gold_customer_lifetime_sales_summary Table:
The .write.mode("overwrite").table("gold_customer_lifetime_sales_summary") command writes the aggregated data to the gold_customer_lifetime_sales_summary table.
The mode("overwrite") specifies that the existing data in the gold_customer_lifetime_sales_summary table will be completely replaced by the new aggregated data.
Conclusion:
When this code is executed, it reads all records from the silver_customer_sales table, performs the specified aggregations grouped by customer_id, and then overwrites the entire gold_customer_lifetime_sales_summary table with the aggregated results. Therefore, option D accurately describes this process: "The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job." Explanation:
The provided PySpark code performs the following operations:
Reads Data from silver_customer_sales Table:
The code starts by accessing the silver_customer_sales table using the spark.table method.
Groups Data by customer_id:
The .groupBy("customer_id") function groups the data based on the customer_id column.
Aggregates Data:
The .agg() function computes several aggregate metrics for each customer_id:
Reference:
PySpark DataFrame groupBy
PySpark Basics
NEW QUESTION # 23
Question-26. There are 5000 different color balls, out of which 1200 are pink color. What is the maximum
likelihood estimate for the proportion of "pink" items in the test set of color balls?
Answer: D
Explanation:
Explanation
Given no additional information, the MLE for the probability of an item in the test set is exactly its frequency
in the training set. The method of maximum likelihood corresponds to many well-known estimation methods
in statistics. For example, one may be interested in the heights of adult female penguins, but be unable to
measure the height of every single penguin in a population due to cost or time constraints. Assuming that the
heights are normally (Gaussian) distributed with some unknown mean and variance, the mean and variance
can be estimated with MLE while only knowing the heights of some sample of the overall population. MLE
would accomplish this by taking the mean and variance as parameters and finding particular parametric values
that make the observed results the most probable (given the model).
In general, for a fixed set of data and underlying statistical model the method of maximum likelihood selects
the set of values of the model parameters that maximizes the likelihood function. Intuitively, this maximizes
the "agreement" of the selected model with the observed data, and for discrete random variables it indeed
maximizes the probability of the observed data under the resulting distribution. Maximum-likelihood
estimation gives a unified approach to estimation, which is well-defined in the case of the normal distribution
and many other problems. However in some complicated problems, difficulties do occur: in such problems,
maximum-likelihood estimators are unsuitable or do not exist.
NEW QUESTION # 24
You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes an average of 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion?
Answer: E
Explanation:
Explanation
Cluster pools allow us to reserve VM's ahead of time, when a new job cluster is created VM are grabbed from the pool. Note: when the VM's are waiting to be used by the cluster only cost incurred is Azure. Databricks run time cost is only billed once VM is allocated to a cluster.
Here is a demo of how to setup and follow some best practices,
https://www.youtube.com/watch?v=FVtITxOabxg&ab_channel=DatabricksAcademy
NEW QUESTION # 25
A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?
Answer: B
Explanation:
Unity Catalog in Databricks provides the ability to attach tags (key-value metadata pairs) to securable objects such as catalogs, schemas, tables, volumes, and functions. Tags are critical for governance, compliance, and automation, as they allow organizations to track metadata like sensitivity, ownership, business purpose, and retention policies directly at the object level.
According to the official Databricks SQL reference for Unity Catalog, the correct way to programmatically add tags to a table is by using the ALTER TABLE ... SET TAGS command. The syntax is:
ALTER TABLE table_name SET TAGS ( ' tag_name ' = ' tag_value ' , ...);
This command can be used within ETL workflows or jobs to automatically apply metadata during or after ingestion, ensuring that governance and compliance rules are embedded in the pipeline itself.
* Option A is correct because it uses the supported syntax for applying tags.
* Option B (APPLY TAGS) is not valid SQL in Unity Catalog and is not recognized by Databricks.
* Option C confuses COMMENT with TAGS. While COMMENT can add descriptive text to a table, it does not handle tags.
* Option D (SET TAGS FOR) is not a valid SQL construct in Databricks for applying tags.
Thus, Option A is the only valid and documented way to programmatically set tags on a table in Unity Catalog.
Reference: Databricks SQL Language Reference - ALTER TABLE ... SET TAGS (Unity Catalog)
NEW QUESTION # 26
......
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