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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. Optimizing transformations for performance 3. Applying data processing logic - Building data pipelines 1. Handling data quality and consistency 2. Developing batch and streaming workflows 3. Implementing ingestion mechanisms
Maintaining and automating data workloads	18%	- Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management - Operationalizing workloads 1. Orchestrating and scheduling jobs 2. Monitoring and troubleshooting pipelines 3. Ensuring reliability and recoverability
Storing and managing data	20%	- Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies - Implementing storage solutions 1. Managing data lifecycle and retention 2. Using data storage services appropriately 3. Designing data warehouses and data lakes
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. Selecting appropriate storage solutions 2. Planning for data security and compliance 3. Designing for reliability, scalability, and efficiency
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. Preparing datasets for querying and reporting 2. Implementing data sharing and governance 3. Optimizing query performance

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Google Data Engineer Sample Questions (Q36-Q41):

NEW QUESTION # 36
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. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
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. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
F. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio

Answer: F

NEW QUESTION # 37
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
B. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
C. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
D. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.

Answer: A

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

NEW QUESTION # 38
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

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

Answer: B

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 # 39
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. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
D. Install the Cloud Storage connector, and store the data in Cloud Storag
E. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
F. Deploy a Cloud Dataproc cluste
G. Change references in scripts from hdfs:// to gs://
H. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
I. Deploy a Cloud Dataproc cluste
J. Use an SSD persistent disk and 50% preemptible worker
K. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance

Answer: F

NEW QUESTION # 40
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A. Denormalize the data as must as possible.
B. Preserve the structure of the data as much as possible.
C. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
D. Use BigQuery UPDATE to further reduce the size of the dataset.
E. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
F. Use BigQuery'ssupport for external data sources to query.

Answer: C,E

NEW QUESTION # 41
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