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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Monitoring and troubleshooting pipelines 2. Orchestrating and scheduling jobs 3. Ensuring reliability and recoverability - Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management
Topic 2: Storing and managing data	20%	- Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns - Implementing storage solutions 1. Designing data warehouses and data lakes 2. Using data storage services appropriately 3. Managing data lifecycle and retention
Topic 3: Ingesting and processing data	25%	- Transforming data 1. Managing schema evolution 2. Optimizing transformations for performance 3. Applying data processing logic - Building data pipelines 1. Developing batch and streaming workflows 2. Implementing ingestion mechanisms 3. Handling data quality and consistency
Topic 4: Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation - Enabling data analysis 1. Optimizing query performance 2. Preparing datasets for querying and reporting 3. Implementing data sharing and governance
Topic 5: Designing data processing systems	24%	- Designing data pipelines 1. Planning for data migration and integration 2. Designing for data transformation and enrichment 3. Defining architecture for batch and streaming processing - Planning data solutions 1. Designing for reliability, scalability, and efficiency 2. Planning for data security and compliance 3. Selecting appropriate storage solutions

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Certification GCP-DE Exam Cost | Valid GCP-DE Exam Answers

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Google Data Engineer Sample Questions (Q17-Q22):

NEW QUESTION # 17
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A. Triggers
B. OutputCriteria
C. Sessions
D. Windows

Answer: A

Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri

NEW QUESTION # 18
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A. Load the data every 30 minutes into a new partitioned table in BigQuery.
B. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
C. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
D. Store the data in a file in a regional Google Cloud Storage bucke
E. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
F. Store the data in Google Cloud Datastor

Answer: A

NEW QUESTION # 19
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

A. The CSV data loaded in BigQuery is not flagged as CSV.
B. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
C. The CSV data has invalid rows that were skipped on import.
D. The CSV data has not gone through an ETL phase before loading into BigQuery.

Answer: C

NEW QUESTION # 20
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Create a cron schedule in Cloud Dataprep.
B. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
C. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
D. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

Answer: B

NEW QUESTION # 21
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

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

Answer: A,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 # 22
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