Databricks-Certified-Professional-Data-Engineer Dumps Guide & Databricks-Certified-Professional-Data-Engineer Latest Test Discount

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Ensuring Data Security and Compliance	10%	- Access control and permissions - Compliance standards implementation - Data encryption and masking
Topic 2: Monitoring and Alerting	10%	- Pipeline observability and logging - Performance and health monitoring - Setting up alerts and notifications
Topic 3: Debugging and Deploying	10%	- Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices
Topic 4: Cost & Performance Optimisation	13%	- Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching
Topic 5: Data Modelling	6%	- Medallion Architecture implementation - Delta Lake table design - Schema design and management
Topic 6: Data Ingestion & Acquisition	7%	- Auto Loader and streaming ingestion - Schema inference and evolution - Connecting to diverse data sources
Topic 7: Developing Code for Data Processing using Python and SQL	22%	- Integration with Databricks APIs and tools - Batch and incremental processing logic - Data transformation and aggregation
Topic 8: Data Governance	7%	- Data lineage and metadata tracking - Unity Catalog management - Policy enforcement
Topic 9: Data Transformation, Cleansing, and Quality	10%	- Handling missing or inconsistent data - Data validation and quality checks - Standardization and normalization
Topic 10: Data Sharing and Federation	5%	- Cross-workspace and cross-cloud access - Unity Catalog data sharing

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q178-Q183):

NEW QUESTION # 178
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

A. Set the skipChangeCommits flag to true on bpm_stats
B. Set the pipelines, reset, allowed property to false on bpm_stats
C. Set the pipelines, reset, allowed property to false on raw_iot
D. Set the SkipChangeCommits flag to true raw_lot

Answer: C

NEW QUESTION # 179
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A. * Total VMs:2
* 200 GB per Executor
* 80 Cores / Executor
B. * Total VMs: 8
* 50 GB per Executor
* 20 Cores / Executor
C. * Total VMs; 1
* 400 GB per Executor
* 160 Cores / Executor
D. * Total VMs: 4
* 100 GB per Executor
* 40 Cores/Executor

Answer: B

Explanation:
This is the correct answer because it is the cluster configuration that will result in maximum performance for a job with at least one wide transformation. A wide transformation is a type of transformation that requires shuffling data across partitions, such as join, groupBy, or orderBy. Shuffling can be expensive and time-consuming, especially if there are too many or too few partitions. Therefore, it is important to choose a cluster configuration that can balance the trade-off between parallelism and network overhead. In this case, having 8 VMs with 50 GB per executor and 20 cores per executor will create 8 partitions, each with enough memory and CPU resources to handle the shuffling efficiently. Having fewer VMs with more memory and cores per executor will create fewer partitions, which will reduce parallelism and increase the size of each shuffle block. Having more VMs with less memory and cores per executor will create more partitions, which will increase parallelism but also increase the network overhead and the number of shuffle files. Verified References: [Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Cluster configurations" section.

NEW QUESTION # 180
Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:

A. Integer programming
B. Apriori Algorithm
C. Ridge and Lasso
D. Ordinary Least squares

Answer: D

Explanation:
Explanation : RY = b0 + b1x1+b2x2+ .... +bnxn
In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters
are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age
and education. In other words, the fitted model should minimize the overall error between the linear model and
the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters

NEW QUESTION # 181
Which statement describes Delta Lake optimized writes?

A. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
B. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
C. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
D. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.

Answer: C

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table. The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.
Reference:
Databricks documentation on Delta Lake performance tuning: https://docs.databricks.com/delta/optimizations/auto-optimize.html

NEW QUESTION # 182
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property"contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

A. DESCRIBE HISTORY dev.pii test
B. DESCRIBE EXTENDED dev.pii test
C. DESCRIBE DETAIL dev.pii test
D. SHOW TBLPROPERTIES dev.pii test
E. SHOW TABLES dev

Answer: B

Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met.
The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property
"contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.

NEW QUESTION # 183
......

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