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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability - Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows
Topic 2: 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 3: 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. ETL/ELT workflows 2. Pub/Sub streaming ingestion
Topic 4: 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

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Google Data Engineer Sample Questions (Q51-Q56):

NEW QUESTION # 51
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

A. Before you test, run a heavy pre-test for several minutes.
B. Do not use a production instance.
C. Use at least 300 GB of data.
D. Run your test for at least 10 minutes.

Answer: B

Explanation:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 52
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. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
C. Preemptible workers cannot store data.
D. Preemptible workers cannot use persistent disk.

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 # 53
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A. Use a standard persistent disk and 50% preemptible worker
B. Change references in scripts from hdfs:// to gs://
C. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
D. Change references in scripts from hdfs:// to gs://
E. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
F. Use an SSD persistent disk and 50% preemptible worker
G. Install the Cloud Storage connector, and store the data in Cloud Storag
H. Deploy a Cloud Dataproc cluste
I. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
J. Deploy a Cloud Dataproc cluste
K. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF

Answer: J

NEW QUESTION # 54
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A. Create a view on the table to present to the virtualization tool.
B. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
C. Export the data into a Google Sheet for virtualization.
D. Create an additional table with only the necessary columns.

Answer: A

NEW QUESTION # 55
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A. Cloud Functions
B. Cloud Dataflow
C. Cloud Scheduler
D. Cloud Composer

Answer: C

NEW QUESTION # 56
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