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| Section | Weight | Objectives |
|---|
| Preparing data for analysis and machine learning | 13% | - Enabling data analysis
- 1. Optimizing query performance
- 2. Implementing data sharing and governance
- 3. Preparing datasets for querying and reporting
- Preparing data for ML
- 1. Feature engineering and data preparation
- 2. Supporting model training and serving
|
| Designing data processing systems | 24% | - Planning data solutions
- 1. Designing for reliability, scalability, and efficiency
- 2. Planning for data security and compliance
- 3. Selecting appropriate storage solutions
- 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
|
| 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. Designing data warehouses and data lakes
- 2. Managing data lifecycle and retention
- 3. Using data storage services appropriately
|
| Ingesting and processing data | 25% | - Transforming data
- 1. Optimizing transformations for performance
- 2. Applying data processing logic
- 3. Managing schema evolution
- Building data pipelines
- 1. Implementing ingestion mechanisms
- 2. Developing batch and streaming workflows
- 3. Handling data quality and consistency
|
| Maintaining and automating data workloads | 18% | - Operationalizing workloads
- 1. Ensuring reliability and recoverability
- 2. Orchestrating and scheduling jobs
- 3. Monitoring and troubleshooting pipelines
- Automation and optimization
- 1. Optimizing resource usage and costs
- 2. Automating deployment and management
|
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Google Data Engineer Sample Questions (Q45-Q50):
NEW QUESTION # 45
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?
- A. Hive
- B. Spark
- C. Pig
- D. YARN
Answer: A,B,C
Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run
NEW QUESTION # 46
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
- A. Create separate views to cover each month, and query from these views
- B. Convert the sharded tables into a single partitioned table
- C. Enable query caching so you can cache data from previous months
- D. Convert all daily log tables into date-partitioned tables
Answer: D
NEW QUESTION # 47
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?
- A. Triggers
- B. OutputCriteria
- C. Windows
- D. Sessions
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 # 48
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?
- A. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
- B. If that number falls below 4000, send an alert.
- C. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
- D. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
- E. Consume the stream of data in Cloud Dataflow using Kafka I
- F. Set a sliding time window of 1 hour every 5 minute
- G. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
- H. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
- I. Consume the stream of data in Cloud Dataflow using Kafka I
- J. If that number falls below 4000, send an alert.
- K. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
- L. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
- M. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
Answer: H
NEW QUESTION # 49
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?
- A. Use Cloud Vision API by providing custom labels as recognition hints.
- B. Use Cloud Vision AutoML with the existing dataset.
- C. Use Cloud Vision AutoML, but reduce your dataset twice.
- D. Train your own image recognition model leveraging transfer learning techniques.
Answer: B
NEW QUESTION # 50
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