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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q55-Q60):

NEW QUESTION # 55
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Windows at every 1 minute
B. Single, Global Window
C. Windows at every 10 minutes
D. Windows at every 100 MB of data

Answer: B

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection

NEW QUESTION # 56
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
B. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
C. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
D. Deploy the Cloud SQL Proxy on the Cloud Dataproc master

Answer: A

NEW QUESTION # 57
Which of these is not a supported method of putting data into a partitioned table?

A. 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".
B. 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.
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 # 58
What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Use a partitioned table; 2) Join tables into one table
B. 1) Use nested repeated fields; 2) Use a partitioned table
C. 1) Join tables into one table; 2) Use nested repeated fields
D. 1) Split table into multiple tables; 2) Use a partitioned table

Answer: C

Explanation:
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

NEW QUESTION # 59
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
B. Use a standard persistent disk and 50% preemptible worker
C. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
D. Deploy a Cloud Dataproc cluste
E. Deploy a Cloud Dataproc cluste
F. Change references in scripts from hdfs:// to gs://
G. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
H. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
I. Change references in scripts from hdfs:// to gs://
J. Install the Cloud Storage connector, and store the data in Cloud Storag
K. Use an SSD persistent disk and 50% preemptible worker

Answer: E

NEW QUESTION # 60
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