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Professional Data Engineer on Google Cloud Platform test Dumps

Google-PDE test Format | Course Contents | Course Outline | test Syllabus | test Objectives

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

The Professional Data Engineer test assesses your ability to:

- Design data processing systems

- Build and operationalize data processing systems

- Operationalize machine learning models

- Ensure solution quality

1. Designing data processing systems

1.1 Selecting the appropriate storage technologies. Considerations include:

- Mapping storage systems to business requirements

- Data modeling

- Tradeoffs involving latency, throughput, transactions

- Distributed systems

- Schema design

1.2 Designing data pipelines. Considerations include:

- Data publishing and visualization (e.g., BigQuery)

- Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)

- Online (interactive) vs. batch predictions

- Job automation and orchestration (e.g., Cloud Composer)

1.3 Designing a data processing solution. Considerations include:

- Choice of infrastructure

- System availability and fault tolerance

- Use of distributed systems

- Capacity planning

- Hybrid cloud and edge computing

- Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)

- At least once, in-order, and exactly once, etc., event processing

1.4 Migrating data warehousing and data processing. Considerations include:

- Awareness of current state and how to migrate a design to a future state

- Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)

- Validating a migration

2. Building and operationalizing data processing systems

2.1 Building and operationalizing storage systems. Considerations include:

- Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)

- Storage costs and performance

- Lifecycle management of data

2.2 Building and operationalizing pipelines. Considerations include:

- Data cleansing

- Batch and streaming

- Transformation

- Data acquisition and import

- Integrating with new data sources

2.3 Building and operationalizing processing infrastructure. Considerations include:

- Provisioning resources

- Monitoring pipelines

- Adjusting pipelines

- Testing and quality control

3. Operationalizing machine learning models

3.1 Leveraging pre-built ML models as a service. Considerations include:

- ML APIs (e.g., Vision API, Speech API)

- Customizing ML APIs (e.g., AutoML Vision, Auto ML text)

- Conversational experiences (e.g., Dialogflow)

3.2 Deploying an ML pipeline. Considerations include:

- Ingesting appropriate data

- Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)

- Continuous evaluation

3.3 Choosing the appropriate training and serving infrastructure. Considerations include:

- Distributed vs. single machine

- Use of edge compute

- Hardware accelerators (e.g., GPU, TPU)

3.4 Measuring, monitoring, and troubleshooting machine learning models. Considerations include:

- Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)

- Impact of dependencies of machine learning models

- Common sources of error (e.g., assumptions about data)

4. Ensuring solution quality

4.1 Designing for security and compliance. Considerations include:

- Identity and access management (e.g., Cloud IAM)

- Data security (encryption, key management)

- Ensuring privacy (e.g., Data Loss Prevention API)

- Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))

4.2 Ensuring scalability and efficiency. Considerations include:

- Building and running test suites

- Pipeline monitoring (e.g., Stackdriver)

- Assessing, troubleshooting, and improving data representations and data processing infrastructure

- Resizing and autoscaling resources

4.3 Ensuring reliability and fidelity. Considerations include:

- Performing data preparation and quality control (e.g., Cloud Dataprep)

- Verification and monitoring

- Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)

- Choosing between ACID, idempotent, eventually consistent requirements

4.4 Ensuring flexibility and portability. Considerations include:

- Mapping to current and future business requirements

- Designing for data and application portability (e.g., multi-cloud, data residency requirements)

- Data staging, cataloging, and discovery

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Professional Data Engineer on Google Cloud Platform
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Question: 230
When a Cloud Bigtable node fails, ____ is lost.
A. all data
B. no data
C. the last transaction
D. the time dimension
Answer: B
Question: 231
Which is not a valid reason for poor Cloud Bigtable performance?
A. The workload isn't appropriate for Cloud Bigtable.
B. The table's schema is not designed correctly.
C. The Cloud Bigtable cluster has too many nodes.
D. There are issues with the network connection.
Answer: C
Question: 232
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?
A. Field promotion
B. Randomization
C. Salting
D. Hashing
Answer: A
Question: 233
When you design a Google Cloud Bigtable schema it is recommended that you _________.
A. Avoid schema designs that are based on NoSQL concepts
B. Create schema designs that are based on a relational database design
C. Avoid schema designs that require atomicity across rows
D. Create schema designs that require atomicity across rows
Answer: C
Question: 234
Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud
Bigtable?
A. You expect to store at least 10 TB of data.
B. You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a
small number of rows.
C. You need to integrate with Google BigQuery.
D. You will not use the data to back a user-facing or latency-sensitive application.
Answer: C
Question: 235
Cloud Bigtable is Google's ______ Big Data database service.
A. Relational
B. mySQL
C. NoSQL
D. SQL Server
Answer: C
Question: 236
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?
A. 500 TB
B. 1 GB
C. 1 TB
D. 500 GB
Answer: C
Question: 237
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. Do not use a production instance.
B. D. Use at least 300 Gof data.
C. Before you test, run a heavy pre-test for several minutes.
D. Use at least 300 GB of data.
Answer: A
Question: 238
Cloud Bigtable is a recommended option for storing very large amounts of ____________________________?
A. multi-keyed data with very high latency
B. multi-keyed data with very low latency
C. single-keyed data with very low latency
D. single-keyed data with very high latency
Answer: C
Question: 239
Google Cloud Bigtable indexes a single value in each row. This value is called the _______.
A. primary key
B. unique key
C. row key
D. master key
Answer: C
Question: 240
What is the HBase Shell for Cloud Bigtable?
A. The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting
tables.
B. The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.
C. The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting
new virtualized instances.
D. The HBase shell is a command-line tool that performs only user account management functions to grant access
to Cloud Bigtable instances.
Answer: B
Question: 241
What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud
Bigtable instance?
A. create a third instance and sync the data from the two storage types via batch jobs
B. export the data from the existing instance and import the data into a new instance
C. run parallel instances where one is HDD and the other is SDD
D. the selection is final and you must resume using the same storage type
Answer: B
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Professional Data Engineer on Google Cloud Platform test Dumps

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