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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Monitoring and troubleshooting pipelines 2. Orchestrating and scheduling jobs 3. Ensuring reliability and recoverability - Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs
Topic 2: 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
Topic 3: 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. Implementing cost optimization strategies 2. Configuring storage for access patterns
Topic 4: Ingesting and processing data	25%	- Transforming data 1. Optimizing transformations for performance 2. Managing schema evolution 3. Applying data processing logic - Building data pipelines 1. Implementing ingestion mechanisms 2. Developing batch and streaming workflows 3. Handling data quality and consistency
Topic 5: Designing data processing systems	24%	- Designing data pipelines 1. Planning for data migration and integration 2. Defining architecture for batch and streaming processing 3. Designing for data transformation and enrichment - 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 (Q35-Q40):

NEW QUESTION # 35
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 a dummy user and grant dataset access to that use
B. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
C. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
D. Use the service account's private key to access the dataset
E. Create groups for your users and give those groups access to the dataset
F. Create a service account and grant dataset access to that accoun

Answer: F

NEW QUESTION # 36
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A. Convert the sharded tables into a single partitioned table
B. Convert all daily log tables into date-partitioned tables
C. Create separate views to cover each month, and query from these views
D. Enable query caching so you can cache data from previous months

Answer: B

NEW QUESTION # 37
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(city)" before the WHERE clause.
B. Change "person" to "city.person".
C. Change "person" to "person.city".
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 # 38
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. In the event of an emergency, use the backup copy of the table.
B. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
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. In the event of an emergency, use the backup copy of the table.
F. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
G. In the event of an emergency, use a point-in-time snapshot to recover the data.
H. Set the BigQuery dataset to be multi-regional
I. Set the BigQuery dataset to be regiona
J. Set the BigQuery dataset to be regiona

Answer: D

NEW QUESTION # 39
Which of these is not a supported method of putting data into a partitioned table?

A. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
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. 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.
D. Create a partitioned table and stream new records to it every day.

Answer: A

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 # 40
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