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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Designing data processing systems	20%	- Data pipeline architecture design 1. Batch vs streaming data processing selection 2. Scalable data ingestion design - Storage and data modeling 1. Data warehouse design using BigQuery 2. Data lake architecture on Google Cloud Storage
Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging - Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines
Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataproc and Spark-based processing 2. Dataflow pipeline development - Data ingestion and transformation 1. ETL/ELT workflows 2. Pub/Sub streaming ingestion
Maintaining and optimizing data and ML solutions	20%	- Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines - Security and governance 1. IAM and access control 2. Data encryption and compliance

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The PassReview offers valid, updated, and real Data Engineer GCP-DE exam practice questions that perfectly and quickly prepare the GCP-DE exam candidates. You can easily pass the challenging Data Engineer GCP-DE Certification Exam. GCP-DE exam practice test questions you will get everything that you need to learn, prepare and pass the valuable GCP-DE certification with good scores.

Google Data Engineer Sample Questions (Q68-Q73):

NEW QUESTION # 68
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

A. Cloud SQL
B. Cloud Datastore
C. Cloud Bigtable
D. BigQuery

Answer: D

NEW QUESTION # 69
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. 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.
B. Write an Apache Beam pipeline that creates a BigQuery table per data
C. Re-create the tables using DD
D. Partition the tables by a column containing a TIMESTAMP or DATE Type.
E. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
F. 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.

Answer: A

NEW QUESTION # 70
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. Change "person" to "person.city".
B. Change "person" to "city.person".
C. Add ", UNNEST(city)" before the WHERE clause.
D. Add ", UNNEST(person)" before the WHERE clause.

Answer: D

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 # 71
Which of these sources can you not load data into BigQuery from?

A. Google Cloud Storage
B. File upload
C. Google Drive
D. Google Cloud SQL

Answer: D

Explanation:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data

NEW QUESTION # 72
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
B. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
C. Use Cloud Deployment Manager to automate access provision.
D. Introduce resource hierarchy to leverage access control policy inheritance.
E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
F. Create distinct groups for various teams, and specify groups in Cloud IAM policies.

Answer: D,F

NEW QUESTION # 73
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