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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 2. Implement batch processing systems 3. Implement streaming data systems - Operationalizing systems 1. Manage logging and alerting 2. Monitor data pipelines and workloads 3. Implement fault tolerance and recovery
Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Optimize storage and compute usage 2. Tune query and pipeline performance 3. Control operational costs - Reliability and scalability 1. Implement disaster recovery strategies 2. Scale data workloads efficiently 3. Design highly available systems
Designing data processing systems	22%-27%	- Designing for data ingestion 1. Select appropriate storage systems 2. Design scalable and reliable ingestion architecture 3. Design batch and streaming ingestion solutions - Designing data pipelines 1. Design orchestration workflows 2. Build ETL and ELT pipelines 3. Optimize pipeline performance and cost
Ensuring solution quality	20%-25%	- Security and compliance 1. Manage IAM and access control 2. Implement encryption and compliance controls 3. Protect sensitive data - Data quality management 1. Implement data governance 2. Validate and clean data 3. Maintain metadata and lineage

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GCP-DE Exam Questions Vce - Reliable GCP-DE Braindumps Pdf

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Google Data Engineer Sample Questions (Q75-Q80):

NEW QUESTION # 75
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 Scheduler
B. Cloud Dataflow
C. Cloud Composer
D. Cloud Functions

Answer: A

NEW QUESTION # 76
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A. Add ", UNNEST(person)" before the WHERE clause.
B. Add ", UNNEST(city)" before the WHERE clause.
C. Change "person" to "city.person".
D. Change "person" to "person.city".

Answer: A

Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu

NEW QUESTION # 77
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A. Create groups for your users and give those groups access to the dataset
B. Create a dummy user and grant dataset access to that use
C. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
D. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
E. Use the service account's private key to access the dataset
F. Create a service account and grant dataset access to that accoun

Answer: F

NEW QUESTION # 78
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A. Add secondary indexes to support query patterns.
B. Use Cloud Dataflow to transform data to support query patterns.
C. Add secondary indexes to support query patterns.
D. Use Cloud Spanner for storag
E. Use Cloud Dataflow to transform data to support query patterns.
F. Use Cloud SQL for storag
G. Use Cloud SQL for storag
H. Use Cloud Spanner for storag

Answer: E

Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform

NEW QUESTION # 79
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A. Partition the tables by a column containing a TIMESTAMP or DATE Type.
B. Write an Apache Beam pipeline that creates a BigQuery table per data
C. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
D. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
E. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
F. Re-create the tables using DD

Answer: D

NEW QUESTION # 80
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