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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q44-Q49):

NEW QUESTION # 44
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. Create separate views to cover each month, and query from these views
B. Convert all daily log tables into date-partitioned tables
C. Enable query caching so you can cache data from previous months
D. Convert the sharded tables into a single partitioned table

Answer: B

NEW QUESTION # 45
Which of the following statements is NOT true regarding Bigtable access roles?

A. You can configure access control only at the project level.
B. To give a user access to only one table in a project, you must configure access through your application.
C. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
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 # 46
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
B. Preemptible workers cannot use persistent disk.
C. A Dataproc cluster cannot have only preemptible workers.
D. Preemptible workers cannot store data.

Answer: C,D

Explanation:
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster: Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits. Reference:
https://cloud.google.com/dataproc/docs/concepts/preemptible-vms

NEW QUESTION # 47
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(person)" before the WHERE clause.
C. Change "person" to "person.city".
D. Add ", UNNEST(city)" before the WHERE clause.

Answer: B

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 # 48
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Keep your row key reasonably short
B. Keep the row keep as an 8 bit integer
C. Include multiple time series values within the row key
D. Keep your row key as long as the field permits

Answer: A

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION # 49
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