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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
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
Storing and managing data	20%	- Implementing storage solutions 1. Designing data warehouses and data lakes 2. Using data storage services appropriately 3. Managing data lifecycle and retention - Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies
Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Preparing datasets for querying and reporting 2. Optimizing query performance 3. Implementing data sharing and governance - Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving
Designing data processing systems	24%	- Designing data pipelines 1. Defining architecture for batch and streaming processing 2. Planning for data migration and integration 3. Designing for data transformation and enrichment - Planning data solutions 1. Selecting appropriate storage solutions 2. Planning for data security and compliance 3. Designing for reliability, scalability, and efficiency
Ingesting and processing data	25%	- Building data pipelines 1. Developing batch and streaming workflows 2. Handling data quality and consistency 3. Implementing ingestion mechanisms - Transforming data 1. Managing schema evolution 2. Optimizing transformations for performance 3. Applying data processing logic

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Google GCP-DE Fresh Dumps | High Pass-Rate GCP-DE Exam Cram Pdf: Data Engineer

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Google Data Engineer Sample Questions (Q37-Q42):

NEW QUESTION # 37
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 WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
B. Use the WHERE_PARTITIONTIME pseudo column
C. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
D. Use the TABLE_DATE_RANGE function

Answer: D

Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap

NEW QUESTION # 38
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A. The with Allowed Lateness method
B. An event time trigger
C. A processing time trigger
D. An hourly watermark

Answer: C

Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers

NEW QUESTION # 39
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A. Preemptible workers cannot store data.
B. A Dataproc cluster cannot have only preemptible workers.
C. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
D. Preemptible workers cannot use persistent disk.

Answer: A,B

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 # 40
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
B. Add a SideInput that returns a Boolean if the element is corrupt.
C. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
D. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

Answer: C

NEW QUESTION # 41
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A. A sequential numeric ID
B. A non-sequential numeric ID
C. A stock symbol followed by a timestamp
D. A timestamp followed by a stock symbol

Answer: A,D

Explanation:
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design] Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti

NEW QUESTION # 42
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