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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q44-Q49):

NEW QUESTION # 44
You need to choose a database for a new project that has the following requirements:
Fully managed
Able to automatically scale up
Transactionally consistent
Able to scale up to 6 TB
Able to be queried using SQL Which database do you choose?

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

Answer: B

NEW QUESTION # 45
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 "city.person".
B. Add ", UNNEST(city)" before the WHERE clause.
C. Add ", UNNEST(person)" before the WHERE clause.
D. Change "person" to "person.city".

Answer: C

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 # 46
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. Write an Apache Beam pipeline that creates a BigQuery table per data
B. Re-create the tables using DD
C. 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.
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. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
F. Partition the tables by a column containing a TIMESTAMP or DATE Type.

Answer: D

NEW QUESTION # 47
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

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

Answer: B

Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive

NEW QUESTION # 48
What are two methods that can be used to denormalize tables in BigQuery?

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

Answer: D

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 # 49
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