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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines - Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging
Topic 2: Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. Data encryption and compliance 2. IAM and access control - Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines
Topic 3: 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. Scalable data ingestion design 2. Batch vs streaming data processing selection
Topic 4: Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing - Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows

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Google Data Engineer Sample Questions (Q25-Q30):

NEW QUESTION # 25
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
B. Merge the transformed tables together with a SQL query
C. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
D. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
E. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
F. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

Answer: A

NEW QUESTION # 26
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 the sharded tables into a single partitioned table
C. Convert all daily log tables into date-partitioned tables
D. Enable query caching so you can cache data from previous months

Answer: C

NEW QUESTION # 27
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

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

Answer: B

NEW QUESTION # 28
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Windows at every 10 minutes
B. Windows at every 1 minute
C. Single, Global Window
D. Windows at every 100 MB of data

Answer: C

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection

NEW QUESTION # 29
Which is not a valid reason for poor Cloud Bigtable performance?

A. The workload isn't appropriate for Cloud Bigtable.
B. The Cloud Bigtable cluster has too many nodes.
C. There are issues with the network connection.
D. The table's schema is not designed correctly.

Answer: B

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 30
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