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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q42-Q47):

NEW QUESTION # 42
Which of these sources can you not load data into BigQuery from?

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

Answer: C

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 # 43
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A. Cloud Natural Language API
B. Dialogflow Enterprise Edition
C. Cloud AutoML Natural Language
D. Cloud Speech-to-Text API

Answer: C

NEW QUESTION # 44
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. Change "person" to "person.city".
C. Add ", UNNEST(city)" before the WHERE clause.
D. Change "person" to "city.person".

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 # 45
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use the default autoscaling setting for worker instances.
B. Use Cloud Dataproc to run your transformation
C. Locate the bottleneck and adjust cluster resources.
D. Resize the number of worker nodes in your cluster via the command line.
E. Configure the job to use non-default Compute Engine machine types when needed.
F. Use Cloud Dataproc to run your transformation
G. Monitor the total execution time for a sampling of job
H. Monitor the job system lag with Stackdrive
I. Use Cloud Dataflow to run your transformation
J. Use Cloud Dataflow to run your transformation
K. Use the diagnose command to generate an operational output archiv
L. Monitor CPU utilization for the cluste

Answer: L

NEW QUESTION # 46
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

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

Answer: B

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 # 47
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