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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Ingesting and processing data	25%	- Transforming data 1. Managing schema evolution 2. Applying data processing logic 3. Optimizing transformations for performance - Building data pipelines 1. Handling data quality and consistency 2. Implementing ingestion mechanisms 3. Developing batch and streaming workflows
Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Preparing datasets for querying and reporting 2. Optimizing query performance 3. Implementing data sharing and governance - Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation
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. Configuring storage for access patterns 2. Implementing cost optimization strategies
Maintaining and automating data workloads	18%	- Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management - Operationalizing workloads 1. Monitoring and troubleshooting pipelines 2. Ensuring reliability and recoverability 3. Orchestrating and scheduling jobs
Designing data processing systems	24%	- Designing data pipelines 1. Defining architecture for batch and streaming processing 2. Designing for data transformation and enrichment 3. Planning for data migration and integration - Planning data solutions 1. Planning for data security and compliance 2. Designing for reliability, scalability, and efficiency 3. Selecting appropriate storage solutions

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Google Data Engineer Sample Questions (Q53-Q58):

NEW QUESTION # 53
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A. Add a new column called FullName to the Users tabl
B. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
C. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
D. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
E. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
F. Use BigQuery to export the data for the table to a CSV fil

Answer: E

NEW QUESTION # 54
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. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
B. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
C. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
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. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
F. Merge the transformed tables together with a SQL query

Answer: B

NEW QUESTION # 55
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

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

Answer: B,C

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 # 56
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A. Create an application that provides an AP
B. Create an application that writes to a Cloud SQL database to store the dat
C. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
D. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
E. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
F. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

Answer: A

NEW QUESTION # 57
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
B. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
C. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project

Answer: B

NEW QUESTION # 58
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