GCP-DE Exam PDF & GCP-DE Formal Test

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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q75-Q80):

NEW QUESTION # 75
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger based on time
B. Trigger based on element size in bytes
C. Trigger based on element count
D. Trigger that is a combination of other triggers

Answer: B

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 76
Which of the following statements is NOT true regarding Bigtable access roles?

A. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
B. To give a user access to only one table in a project, you must configure access through your application.
C. You can configure access control only at the project level.
D. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.

Answer: D

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 77
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A. Use BigQuery UPDATE to further reduce the size of the dataset.
B. Use BigQuery'ssupport for external data sources to query.
C. Denormalize the data as must as possible.
D. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
E. Preserve the structure of the data as much as possible.
F. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil

Answer: D,F

NEW QUESTION # 78
What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Use nested repeated fields; 2) Use a partitioned table
B. 1) Split table into multiple tables; 2) Use a partitioned table
C. 1) Join tables into one table; 2) Use nested repeated fields
D. 1) Use a partitioned table; 2) Join tables into one 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 # 79
When a Cloud Bigtable node fails, is lost.

A. the last transaction
B. the time dimension
C. no data
D. all data

Answer: C

Explanation:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 80
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