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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Ensuring reliability and recoverability 2. Monitoring and troubleshooting pipelines 3. Orchestrating and scheduling jobs - Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs
Topic 2: Storing and managing data	20%	- Implementing storage solutions 1. Managing data lifecycle and retention 2. Using data storage services appropriately 3. Designing data warehouses and data lakes - Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies
Topic 3: Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Preparing datasets for querying and reporting 2. Implementing data sharing and governance 3. Optimizing query performance - Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving
Topic 4: Ingesting and processing data	25%	- Building data pipelines 1. Implementing ingestion mechanisms 2. Developing batch and streaming workflows 3. Handling data quality and consistency - Transforming data 1. Applying data processing logic 2. Optimizing transformations for performance 3. Managing schema evolution
Topic 5: Designing data processing systems	24%	- Designing data pipelines 1. Planning for data migration and integration 2. Designing for data transformation and enrichment 3. Defining architecture for batch and streaming processing - Planning data solutions 1. Selecting appropriate storage solutions 2. Designing for reliability, scalability, and efficiency 3. Planning for data security and compliance

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Google Data Engineer Sample Questions (Q45-Q50):

NEW QUESTION # 45
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
B. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
C. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
D. Add a SideInput that returns a Boolean if the element is corrupt.

Answer: A

NEW QUESTION # 46
Which of these is not a supported method of putting data into a partitioned table?

A. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
B. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
C. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
D. Create a partitioned table and stream new records to it every day.

Answer: C

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION # 47
What are all of the BigQuery operations that Google charges for?

A. Storage, queries, and loading data from a file
B. Storage, queries, and streaming inserts
C. Queries and streaming inserts
D. Storage, queries, and exporting data

Answer: B

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION # 48
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. Set the BigQuery dataset to be regiona
B. In the event of an emergency, use the backup copy of the table.
C. Set the BigQuery dataset to be multi-regional
D. In the event of an emergency, use a point-in-time snapshot to recover the data.
E. Set the BigQuery dataset to be regiona
F. Set the BigQuery dataset to be multi-regional
G. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
H. In the event of an emergency, use a point-in-time snapshot to recover the data.
I. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
J. In the event of an emergency, use the backup copy of the table.

Answer: H

NEW QUESTION # 49
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A. Cloud Dataflow
B. Cloud Scheduler
C. Cloud Functions
D. Cloud Composer

Answer: B

NEW QUESTION # 50
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