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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and operationalizing data processing systems | 24% | - Data processing and transformation
|
| Topic 2: Operationalizing machine learning models | 26% | - ML pipeline integration
|
| Topic 3: Ensuring solution quality | 28% | - Security and governance
|
| Topic 4: Designing data processing systems | 22% | - Batch and streaming data processing design
|
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NEW QUESTION # 408
Your chemical company needs to manually check documentation for customer order. You use a pull subscription in Pub/Sub so that sales agents get details from the order. You must ensure that you do not process orders twice with different sales agents and that you do not add more complexity to this workflow.
What should you do?
Answer: B
Explanation:
Pub/Sub exactly-once delivery is a feature that guarantees that subscriptions do not receive duplicate deliveries of messages based on a Pub/Sub-defined unique message ID. This feature is only supported by the pull subscription type, which is what you are using in this scenario. By enabling exactly-once delivery, you can ensure that each order is processed only once by a sales agent, and that no order is lost or duplicated. This also simplifies your workflow, as you do not need to create a separate database or subscription to monitor the pending or processed messages.
NEW QUESTION # 409
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?
Answer: C,D
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 # 410
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet- of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required. You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)
Answer: A,D,F
Explanation:
A). Redis is a key-value store (and in many cases used as in-memory and non persistent cache).
It is not designed for "100TB per year" of highly available storage.
B). HBase is similar to Google Bigtable, fits the requirements perfectly: highly available, scalable and with very low latency.
C). MySQL is a relational DB, designed precisely for ACID transactions and not for the stated requirements.
Also, growth may be an issue.
D). MongoDB is a document-db used for high volume data and maintains currently used data in RAM, so performance is usually really good. Should also fit the requirements well.
E). Cassandra is designed precisely for highly available massive datasets, and a fine tuned cluster may offer low latency in reads. Fits the requirements.
F). HDFS with Hive is great for OLAP and data-warehouse scenarios, allowing to solve map- reduce problems using an SQL subset, but the latency is usually really high (we may talk about seconds, not milliseconds, when obtaining results), so this does not complies with the requirements.
NEW QUESTION # 411
You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?
Answer: B
Explanation:
Timestamp at starting of rowkey causes bottleneck issues.
NEW QUESTION # 412
You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?
Answer: A
NEW QUESTION # 413
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