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Google Certified Professional Data Engineer Exam Sample Questions (Q209-Q214):

NEW QUESTION # 209
You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?

A. Insert output sinks after each key processing step, and observe the writing throughput of each block.
B. Verify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks
C. Log debug information in each ParDo function, and analyze the logs at execution time.
D. Insert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.

Answer: D

Explanation:
A Reshuffle operation is a way to force Dataflow to split the pipeline into multiple stages, which can help isolate the performance of each step and identify bottlenecks. By monitoring the execution details in the Dataflow console, you can see the time, CPU, memory, and disk usage of each stage, as well as the number of elements and bytes processed. This can help you diagnose where the pipeline is slowing down and optimize it accordingly. Reference:
1: Reshuffling your data
2: Monitoring pipeline performance using the Dataflow monitoring interface
3: Optimizing pipeline performance

NEW QUESTION # 210
Which software libraries are supported by Cloud Machine Learning Engine?

A. TensorFlow and Torch
B. Theano and Torch
C. Theano and TensorFlow
D. TensorFlow

Answer: D

Explanation:
Explanation
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.
Reference: https://cloud.google.com/ml-engine/docs/technical-overview#what_it_does

NEW QUESTION # 211
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
B. Use the WHERE_PARTITIONTIME pseudo column
C. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
D. Use the TABLE_DATE_RANGE function

Answer: D

Explanation:
Legacy sql uses table date range whereas standard sql uses table_sufix for wildcard.

NEW QUESTION # 212

A. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
B. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?
C. Create and share a new dataset and view that provides the aggregate results.
D. Create and share an authorized view that provides the aggregate results.
E. Create and share a new dataset and table that contains the aggregate results.

Answer: A

NEW QUESTION # 213
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

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

Answer: C

Explanation:
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 # 214
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