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| Section | Weight | Objectives |
|---|---|---|
| Data Processing with Spark | 25-30% | - Python and SQL for data engineering
|
| Delta Lake | 20-25% | - Delta Lake fundamentals
|
| Data Warehouse and Lakehouse Architecture | 15-20% | - Lakehouse architecture principles
|
| Pipeline Development and Orchestration | 10-15% | - Databricks workflows
|
| Data Ingestion | 15-20% | - Streaming ingestion
|
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NEW QUESTION # 51
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.
address THEN
UPDATE SET current = false, end_date = staged_updates.effective_date
WHEN NOT MATCHED THEN
INSERT (customer_id, address, current, effective_date, end_date)
VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?
* The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
Answer: A
Explanation:
The providedMERGEstatement is a classic implementation of a Type 2 SCD in a data warehousing context.
In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in thecustomerstable is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.References:
Databricks documentation on implementing SCDs using Delta Lake and theMERGEstatement (https://docs.
databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).
NEW QUESTION # 52
Review the following error traceback:
Which statement describes the error being raised?
Answer: C
Explanation:
Explanation
The error is a Py4JJavaError, which means that an exception was thrown in Java code called by Python code using Py4J. Py4J is a library that enables Python programs to dynamically access Java objects in a Java Virtual Machine (JVM). PySpark uses Py4J to communicate with Spark's JVM-based engine. The error message shows that the exception was thrown by org.apache.spark.sql.AnalysisException, which means that an error occurred during the analysis phase of Spark SQL query processing. The error message also shows that the cause of the exception was "cannot resolve 'heartrateheartrateheartrate' given input columns". This means that Spark could not find a column named heartrateheartrateheartrate in the input DataFrame or Dataset. The reason for this error is that there is a syntax error in the code that caused this exception. The code is:
df.withColumn("heartrate", heartrate * 3)
The code tries to create a new column called heartrate by multiplying an existing column called heartrate by 3.
However, the code does not correctly identify the heartrate column as a column object, but rather as a plain Python variable. This causes PySpark to concatenate the variable name with itself three times, resulting in heartrateheartrateheartrate, which is not a valid column name. To fix this error, the code should use one of the following ways to identify the heartrate column as a column object:
df.withColumn("heartrate", df["heartrate"] * 3) df.withColumn("heartrate", df.heartrate * 3) df.withColumn("heartrate", col("heartrate") * 3) Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Py4J Documentation, under "What is Py4J?"; Databricks Documentation, under "Query plans - Analysis phase"; Databricks Documentation, under "Accessing columns".
NEW QUESTION # 53
Which of the following is true of Delta Lake and the Lakehouse?
Answer: D
Explanation:
Explanation
https://docs.delta.io/2.0.0/table-properties.html
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters1. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer1. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan1. This can significantly improve the query performance and reduce the I/O cost.
The other options are false because:
Parquet compresses data column by column, not row by row2. This allows for better compression ratios, especially for repeated or similar values within a column2.
Views in the Lakehouse do not maintain a valid cache of the most recent versions of source tables at all times3. Views are logical constructs that are defined by a SQL query on one or more base tables3. Views are not materialized by default, which means they do not store any data, but only the query definition3. Therefore, views always reflect the latest state of the source tables when queried3.
However, views can be cached manually using the CACHE TABLE or CREATE TABLE AS SELECT commands.
Primary and foreign key constraints can not be leveraged to ensure duplicate values are never entered into a dimension table. Delta Lake does not support enforcing primary and foreign key constraints on tables. Constraints are logical rules that define the integrity and validity of the data in a table. Delta Lake relies on the application logic or the user to ensure the data quality and consistency.
Z-order can be applied to any values stored in Delta Lake tables, not only numeric values. Z-order is a technique to optimize the layout of the data files by sorting them on one or more columns. Z-order can improve the query performance by clustering related values together and enabling more efficient data skipping. Z-order can be applied to any column that has a defined ordering, such as numeric, string, date, or boolean values.
References: Data Skipping, Parquet Format, Views, [Caching], [Constraints], [Z-Ordering]
NEW QUESTION # 54
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources.
Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table.
Given the current implementation, which method can be used?
Answer: C
Explanation:
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.
References:
* Delta Lake Documentation on Time Travel: Delta Time Travel
* Delta Lake Versioning: Delta Lake Versioning Guide
NEW QUESTION # 55
A transactions table has been liquid clustered on the columns product_id, user_id, and event_date.
Which operation lacks support for cluster on write?
Answer: A
Explanation:
Delta Lake's Liquid Clustering is an advanced feature that improves query performance by dynamically clustering data without requiring costly compaction steps like traditional Z-ordering.
When performing writes to a Liquid Clustered table, some write operations automatically maintain clustering, while others do not.
Explanation of Each Option:
(A) spark.writestream.format('delta').mode('append') (Correct Answer)
Reason: Streaming writes (writestream) do not support Liquid Clustering because streaming data arrives in micro-batches.
Since Liquid Clustering needs efficient global reorganization of files, streaming append operations don't provide sufficient data volume at a time to be effectively clustered.
Delta Lake documentation states that Liquid Clustering is only supported for batch writes.
(B) CTAS and RTAS statements
Reason: CREATE TABLE AS SELECT (CTAS) and REPLACE TABLE AS SELECT (RTAS) are batch operations and can enforce Liquid Clustering.
These operations create or replace a table based on a query result, and since they are batch-based, Liquid Clustering applies.
(C) INSERT INTO operations
Reason: INSERT INTO is supported for Liquid Clustering because it is a batch operation.
While it may not be as efficient as MERGE or COPY INTO, clustering is applied upon execution.
(D) spark.write.format('delta').mode('append')
Reason: Batch append operations are supported for Liquid Clustering.
Unlike streaming append, batch writes allow the optimizer to re-cluster data efficiently.
Conclusion:
Since streaming append operations do not support Liquid Clustering, option (A) is the correct answer.
Reference:
Liquid Clustering in Delta Lake - Databricks Documentation
NEW QUESTION # 56
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